MAG — S2 Exploratory Data Analysis

1 WRANGLING

Initial pilot data were collected via Qualtrics surveys named MAG_S2_PROLIFIC-DATACOLLAR_0 and MAG_S2_PROLIFIC-DATACOLLAR_0, each presenting participants with one common graph (“block 0”, STIMULUS = B0-0) and four subsequent graphs in random order (“block 1”).

Subsequently we made minor alterations to the question wording and respose wording, and the remaining stimuli were organized in 6 blocks. Each block was collected via PROLIFIC using independent Qualtrics surveys, with the exception of a sample of respondents directly recruited from Tumblr, using a single Qualtrics surveys with randomization logic to assign each Tumblr participant to one of the six stimulus blocks.

Participants were excluded from the sample for the following reasons

• abandoned; Finished = False and Progress < 100%
• fails consent or pre-screening, Finished = TRUE, Progress = 100%, Q_TerminateFlag = “Screened”
• didnot-follow-instructions free responses indicate they misinterpred the question (most commonly they describe the graph rather than their reactions/judgements/impressions of it)
• illegible-english free responses text is largely illegible

Following wrangling, there should be 6 blocks of stimuli, each containing responses from 40 participants from prolific, and TODO responses directly from Tumblr.

1.0.1 Import Configuration Files

############## IMPORT STIMULI FILE
df_stimuli <- read_csv("data/input/stimuli.csv", col_names = TRUE) %>% 
  mutate(
    BLOCK = as.factor(BLOCK), 
    STIMULUS_CATEGORY = as.factor(CATEGORY),
    ID = as.factor(ID),
    MAKER_ID = as.factor(MAKER_ID)
  )

############## BUILD LABELS
ref_stimuli <- levels(df_stimuli$ID)
ref_sd_questions <- c("MAKER_DESIGN","MAKER_DATA","MAKER_POLITIC",
               "MAKER_ARGUE","MAKER_SELF","MAKER_ALIGN","MAKER_TRUST",
               "CHART_LIKE", "CHART_BEAUTY", "CHART_INTENT", "CHART_TRUST")
left <- c("professional","professional","left-leaning","confrontational",
          "altruistic","does NOT share","untrustworthy",
          "NOT at all","NOT at all", "inform", "untrustworthy")
right <- c("layperson","layperson","right-leaning","diplomatic",
           "selfish", "DOES share", "trustworthy",
           "very much", "very much", "persuade", "trusthworthy")
ref_labels <- as.data.frame(cbind(left,right))
rownames(ref_labels) <- ref_sd_questions
ref_blocks <- c("block1", "block2", "block3", "block4", "block5", "block6")
rm(left,right)

############## STUDY ID FILE
#most blocks were run as separate qualtrics surveys with diffferent recruitments in Prolific
#Tumblr was run with all blocks and randomization
df_studies <- read_csv("data/input/studies.csv", col_names = TRUE) %>%
  mutate(
    ID.Study = as.factor(ID.Study),
    Assigned.Block = as.factor(Assigned.Block),
    Distribution = as.factor(Distribution),
    Prolific.Name = as.factor(Prolific.Name),
    Qualtrics.URL = as.factor(Qualtrics.URL),
    Qualtrics.Survey = as.factor(Qualtrics.Survey),
    Sample = as.factor(Sample),
    Scope = as.factor(Scope)
)

1.0.2 Import Data Files

#1. IMPORT RAW DATA FILES ########################################################
#### RAW DATA ####################################################################
# will always be the unaltered version of imported data
# 1 row per subject 
df_raw_datacollar <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_datacollarpilot_B1.csv", col_names = TRUE)
df_raw_bluecollar <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_bluecollarpilot_B1.csv", col_names = TRUE)
df_raw_b1 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B1.csv", col_names = TRUE)
df_raw_b2 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B2.csv", col_names = TRUE)
df_raw_b3 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B3.csv", col_names = TRUE)
df_raw_b4 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B4.csv", col_names = TRUE)
df_raw_b5 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B5.csv", col_names = TRUE)
df_raw_b6 <- read_csv("data/input/CLEAN_MAG_S2_PROLIFIC_GENERAL_B6.csv", col_names = TRUE)
df_raw_tumblr_paid <- read_csv("data/input/CLEAN_MAG_S2_TUMBLR_PAID_ALL.csv", col_names = TRUE)
df_raw_tumblr_free <- read_csv("data/input/CLEAN_MAG_S2_TUMBLR_FREE_ALL.csv", col_names = TRUE)

# drop first two rows (qualtrics_specs)
df_raw_datacollar <- df_raw_datacollar[-c(1:2),]
df_raw_bluecollar <- df_raw_bluecollar[-c(1:2),]
df_raw_b1 <- df_raw_b1[-c(1:2),]
df_raw_b2 <- df_raw_b2[-c(1:2),]
df_raw_b3 <- df_raw_b3[-c(1:2),]
df_raw_b4 <- df_raw_b4[-c(1:2),]
df_raw_b5 <- df_raw_b5[-c(1:2),]
df_raw_b6 <- df_raw_b6[-c(1:2),]
df_raw_tumblr_paid <- df_raw_tumblr_paid[-c(1:2),]
df_raw_tumblr_free <- df_raw_tumblr_free[-c(1:2),]

# ADD DUMMY COLS TO DATA/BLUE COLLAR PILOT DATA 
# necessary b/c pilot (block 1) did not have chart_action behavioural question
# x <- compare_df_cols(df_raw_b2, df_raw_pilot)
df_raw_pilot <- rbind(df_raw_datacollar, df_raw_bluecollar) %>% 
  mutate(
    '0_Q_B0_CHART_ACTION' = NA,
    '1_Q_B1_CHART_ACTION' = NA,
    '1_Q_B2_CHART_ACTION' = NA,
    '1_Q_B3_CHART_ACTION' = NA,
    '1_Q_B4_CHART_ACTION' = NA,
    '1_Q_B5_CHART_ACTION' = NA,
    '1_Q_B6_CHART_ACTION' = NA,
    '2_Q_B1_CHART_ACTION' = NA,
    '2_Q_B2_CHART_ACTION' = NA,
    '2_Q_B3_CHART_ACTION' = NA,
    '2_Q_B4_CHART_ACTION' = NA,
    '2_Q_B5_CHART_ACTION' = NA,
    '2_Q_B6_CHART_ACTION' = NA,
    '3_Q_B1_CHART_ACTION' = NA,
    '3_Q_B2_CHART_ACTION' = NA,
    '3_Q_B3_CHART_ACTION' = NA,
    '3_Q_B4_CHART_ACTION' = NA,
    '3_Q_B5_CHART_ACTION' = NA,
    '3_Q_B6_CHART_ACTION' = NA,
    '4_Q_B1_CHART_ACTION' = NA,
    '4_Q_B2_CHART_ACTION' = NA,
    '4_Q_B3_CHART_ACTION' = NA,
    '4_Q_B4_CHART_ACTION' = NA,
    '4_Q_B5_CHART_ACTION' = NA,
    '4_Q_B6_CHART_ACTION' = NA
  )

# BIND COLUMNS RAW prolific datasets 
df_raw_prolific <- rbind(df_raw_pilot, df_raw_b1, df_raw_b2, df_raw_b3, df_raw_b4, df_raw_b5, df_raw_b6)

# RETROFIT SOME COLNAMES FOR COMPATIBILITY WITH TUMBLR 
df_raw_prolific <- df_raw_prolific %>% 
  #drop T_BROWSER cols [these are blank]
  select(-contains("T_BROWSER"), -T_EMAIL) %>% 
  mutate(
    Q_RelevantIDDuplicate = NA, 
    Q_RelevantIDDuplicateScore = NA, 
    Q_RelevantIDFraudScore = NA, 
    Q_RelevantIDLastStartDate = NA, 
    RANDOM_BLOCK = NA
  ) %>% rename_with(
    stringr::str_replace, 
      pattern = "P_BROWSER", replacement = "BROWSER",
  ) %>% 
  rename(
    BROWSER_OS = `BROWSER_Operating System`
  ) %>% mutate_all(funs(str_replace(., "millenial", "millennial")))


# BIND COLUMNS RAW prolific datasets 
df_raw_tumblr <- rbind(df_raw_tumblr_paid, df_raw_tumblr_free)

# RETROFIT SOME TUMBLR COLNAMES FOR MERGING WITH PROLIFIC 
df_raw_tumblr <- df_raw_tumblr %>% 
  select( - t_example_question1, -t_example_question2) %>% 
  mutate(
    #add empty browser cols; didn't collect these in tumblr qualtrics
    BROWSER_Browser = NA,
    BROWSER_OS = NA,
    BROWSER_Resolution = NA,
    BROWSER_Version = NA, 
    ID_PROLIFIC = "TUMBLR",
    ID_SESSION = "TUMBLR",
    ID_STUDY = "TUMBLR"
  ) %>% mutate_all(funs(str_replace(., "millenial", "millennial")))

#dataframe column comparisons 
# x <- janitor::compare_df_cols(df_raw_prolific, df_raw_tumblr)

# JOINT PROLIFIC AND TUMBLR DATAFRAMES
df_raw <- rbind(df_raw_prolific, df_raw_tumblr)

df_raw <- df_raw %>% 
  #reorder 
  select( RANDOM_BLOCK, 
          End_State,
          StartDate:randomize_common, 
          ID_PROLIFIC:ID_SESSION, 
          D_gender:FEEDBACK, 
          PROLIFIC_PID: FL_14_DO,
          Q_RelevantIDDuplicate: Q_RelevantIDLastStartDate, 
          `0_Q_B0_ENCOUNTER`: `0_Q_B0_CHART_LATENCY_Click Count`, 
          `0_Q_B0_CHART_ACTION`, 
          `1_Q_B1_loop-number` : `1_Q_B1_CHART_LATENCY_Click Count`,
          `1_Q_B1_CHART_ACTION`, 
          `2_Q_B1_loop-number` : `2_Q_B1_CHART_LATENCY_Click Count`,
          `2_Q_B1_CHART_ACTION`, 
          `3_Q_B1_loop-number` : `3_Q_B1_CHART_LATENCY_Click Count`,
          `3_Q_B1_CHART_ACTION`, 
          `4_Q_B1_loop-number` : `4_Q_B1_CHART_LATENCY_Click Count`,
          `4_Q_B1_CHART_ACTION`, 
          `1_Q_B2_loop-number` : `1_Q_B2_CHART_LATENCY_Click Count`,
          `1_Q_B2_CHART_ACTION`, 
          `2_Q_B2_loop-number` : `2_Q_B2_CHART_LATENCY_Click Count`,
          `2_Q_B2_CHART_ACTION`, 
          `3_Q_B2_loop-number` : `3_Q_B2_CHART_LATENCY_Click Count`,
          `3_Q_B2_CHART_ACTION`, 
          `4_Q_B2_loop-number` : `4_Q_B2_CHART_LATENCY_Click Count`,
          `4_Q_B2_CHART_ACTION`, 
          `1_Q_B3_loop-number` : `1_Q_B3_CHART_LATENCY_Click Count`,
          `1_Q_B3_CHART_ACTION`, 
          `2_Q_B3_loop-number` : `2_Q_B3_CHART_LATENCY_Click Count`,
          `2_Q_B3_CHART_ACTION`, 
          `3_Q_B3_loop-number` : `3_Q_B3_CHART_LATENCY_Click Count`,
          `3_Q_B3_CHART_ACTION`, 
          `4_Q_B3_loop-number` : `4_Q_B3_CHART_LATENCY_Click Count`,
          `4_Q_B3_CHART_ACTION`, 
          `1_Q_B4_loop-number` : `1_Q_B4_CHART_LATENCY_Click Count`,
          `1_Q_B4_CHART_ACTION`, 
          `2_Q_B4_loop-number` : `2_Q_B4_CHART_LATENCY_Click Count`,
          `2_Q_B4_CHART_ACTION`, 
          `3_Q_B4_loop-number` : `3_Q_B4_CHART_LATENCY_Click Count`,
          `3_Q_B4_CHART_ACTION`, 
          `4_Q_B4_loop-number` : `4_Q_B4_CHART_LATENCY_Click Count`,
          `4_Q_B4_CHART_ACTION`, 
          `1_Q_B5_loop-number` : `1_Q_B5_CHART_LATENCY_Click Count`,
          `1_Q_B5_CHART_ACTION`, 
          `2_Q_B5_loop-number` : `2_Q_B5_CHART_LATENCY_Click Count`,
          `2_Q_B5_CHART_ACTION`, 
          `3_Q_B5_loop-number` : `3_Q_B5_CHART_LATENCY_Click Count`,
          `3_Q_B5_CHART_ACTION`, 
          `4_Q_B5_loop-number` : `4_Q_B5_CHART_LATENCY_Click Count`,
          `4_Q_B5_CHART_ACTION`, 
          `1_Q_B6_loop-number` : `1_Q_B6_CHART_LATENCY_Click Count`,
          `1_Q_B6_CHART_ACTION`, 
          `2_Q_B6_loop-number` : `2_Q_B6_CHART_LATENCY_Click Count`,
          `2_Q_B6_CHART_ACTION`, 
          `3_Q_B6_loop-number` : `3_Q_B6_CHART_LATENCY_Click Count`,
          `3_Q_B6_CHART_ACTION`, 
          `4_Q_B6_loop-number` : `4_Q_B6_CHART_LATENCY_Click Count`,
          `4_Q_B6_CHART_ACTION`
          #df_raw 937 vars           
  ) 



#DROP WIP DATAFRAMES 
rm(df_raw_datacollar, df_raw_bluecollar, df_raw_b1, df_raw_b2, df_raw_b3, df_raw_b4, df_raw_b5, df_raw_b6, df_raw_pilot, df_raw_prolific, df_raw_tumblr, df_raw_tumblr_free, df_raw_tumblr_paid)

1.0.3 Clean Data

#2. CLEAN MASTER PARTICIPANT-LEVEL DF  #########################################################
################################################################################################
#### MASTER WIDE FORMAT DATA FRAME [1 row / qualtrics submission] ################

df_data <- df_raw %>% 
  select(
    -EndDate, -IPAddress, -RecordedDate,
    -RecipientLastName, -RecipientFirstName, -RecipientEmail,
    -ExternalReference, -LocationLatitude, -LocationLongitude, 
    -DistributionChannel, -UserLanguage, -Q_RecaptchaScore,
    -BROWSER_Version, -BROWSER_Resolution, 
    -CONSENT, -ELIGIBILITY, 
    -randomize_common,
    #hidden q that controls common stimulus url
    -stimulus_common, 
    #not actually randomization order
    -FL_14_DO, 
    -contains("First Click"), -contains("Last Click"), -contains("Click Count"),
    -D_politicalParty_DO, 
    -ID_PROLIFIC, -ID_STUDY, -ID_SESSION #redundant to other cols 
  ) %>% 
  rename(
    duration.sec = `Duration (in seconds)`,
    EndState = End_State, 
    TerminateFlag = Q_TerminateFlag,
    Source = Status, #where the survey originated from (should not be preview or test)
    PLATFORM = Q_PLATFORM,
    ID.Qualtrics = ResponseId,
    ID.Prolific = PROLIFIC_PID,
    ID.Study = STUDY_ID,
    ID.Session = SESSION_ID,
    # P_BROWSER_OS = `P_BROWSER_Operating System`,
    # T_BROWSER_OS = `T_BROWSER_Operating System`,
    SCREEN_workFunction_TEXT = SCREEN_workFunction_22_TEXT, 
    SCREEN_socialMedia_TEXT = SCREEN_socialMedia_18_TEXT,
    D_politicalParty_OTHER = D_politicalParty_4_TEXT,
    D_politicsSocial = D_politicsSocial_1,
    D_politicsFiscal = D_politicsFiscal_2
 ) %>% 
  mutate(
    #SET FACTORS 
    D_politicsSocial = as.numeric(D_politicsSocial),
    D_politicsFiscal = as.numeric(D_politicsFiscal),
    ID.Study = factor(ID.Study),
    ID.Qualtrics = factor(ID.Qualtrics),
    ID.Prolific = factor(ID.Prolific),
    ID.Session = factor(ID.Session),
    PLATFORM = factor(PLATFORM),
    Source = factor(Source),
    Finished = as.logical(Finished),
    TerminateFlag = factor(TerminateFlag),
    EndState = factor(EndState), 
    D_gender = factor(D_gender), 
    D_age = factor(D_age), 
    D_income = factor(D_income, 
                      levels = c(
                        "Prefer not to say",
                        "Less than $25,000",
                        "$25,000-$49,999"  ,
                        "$50,000-$74,999"  ,
                        "$75,000-$99,999"  ,
                        "$100,000-$149,999",
                        "$150,000 or more" 
                      )),
    D_employmentStatus = factor(D_employmentStatus),
    duration.sec = as.numeric(duration.sec), #weird booleans should only be for the test generator
    duration.min = round(duration.sec/60,2),
    Progress = as.numeric(Progress), 
    D_education = forcats::fct_na_value_to_level( D_education, level="NA"),
    D_education = forcats::fct_collapse( D_education,
                                no_data = "NA",
                                less_high_school = c("Some high school or less"),
                                high_school = c("High school diploma or GED"),
                                some_college = c("Some college, but no degree", "Some college, no degree"),
                                associates =c( "Associates or technical degree"),
                                undergrad = c("Bachelor’s degree","Bachelor's degree"),
                                grad = c("Graduate or professional degree (MA, MS, MBA, PhD, JD, MD, DDS etc.)",
                                         "Graduate or professional degree (MA, MS, MBA, PhD, JD, MD, DDS, etc)")
    ),
    D_education = factor(D_education, 
                                       levels = c("no_data", "less_high_school","high_school", "some_college",
                                         "associates", "undergrad", "grad"), 
                                       labels = c("NA", "some high school or less","high school diploma or GED ", 
                                                  "some college", "associates or technical degree", 
                                                  "undergradudate degree", "graduate or professional degree"), 
                                       ),
    D_politicalParty = factor(D_politicalParty, levels = c("No preference", "Other", "Independent", "Republican", "Democrat")),
    D_age = factor(D_age, 
                   levels = c("18-24 years old" ,
                              "25-34 years old" ,
                              "35-44 years old" ,
                              "45-54 years old" ,
                              "55-64 years old" ,
                              "65+ years old"   ), 
                   labels = c("18-24", "25-34","35-44","45-54","55-64","65+ years"))
  ) %>%
  #REPLACE RANDOM TRAILING _1 AND _65 FROM QUALTRICS
  rename_with( .cols = contains('_65'), .fn = ~str_replace(.,  pattern = '_65', replacement = '')) %>% 
  rename_with( .cols = contains('_1'), .fn = ~str_replace(.,  pattern = '_1', replacement = '')) %>% 
  #RM _PAGE Submit from LATENCY
  rename_with( .cols = contains('_Page Submit'), .fn = ~str_replace(.,  pattern = '_Page Submit', replacement = '')) %>% 
  #CHANGE DETAIL QUESTION DELIMITER FOR PIVOT PURPOSES 
  rename_with( .cols = contains('_CHART_'), .fn = ~str_replace(.,  pattern = '_CHART_', replacement = '_CHART-')) %>% 
  #CHANGE DETAIL QUESTION DELIMITER FOR PIVOT PURPOSES 
  rename_with( .cols = contains('_MAKER_'), .fn = ~str_replace(.,  pattern = '_MAKER_', replacement = '_MAKER-')) %>% 
  #CHANGE DETAIL QUESTION DELIMITER FOR PIVOT PURPOSES 
  rename_with( .cols = contains('_AGE_'), .fn = ~str_replace(.,  pattern = '_AGE_', replacement = '_AGE-')) %>% 
  #CHANGE DETAIL QUESTION DELIMITER FOR PIVOT PURPOSES 
  rename_with (.cols = contains('_GENDER_'), .fn = ~str_replace(., pattern = '_GENDER_', replacement = '_GENDER-')) %>% 
  #CHANGE DETAIL QUESTION DELIMITER FOR PIVOT PURPOSES 
  rename_with (.cols = contains('_TOOL_'), .fn = ~str_replace(., pattern = '_TOOL_', replacement = '_TOOL-')) %>% 
  select(
    #reordering
    RANDOM_BLOCK:Progress, 
    Finished, EndState, TerminateFlag, 
    Q_RelevantIDDuplicate:Q_RelevantIDLastStartDate,
    ID.Qualtrics, 
    ID.Prolific : ID.Session, 
    duration.sec, duration.min,
    BROWSER_Browser : BROWSER_OS, 
    D_gender:D_politicsFiscal,
    SCREEN_workMethod: SCREEN_socialMedia_TEXT, 
    PURPOSE, FEEDBACK, PLATFORM,
    `0_Q_B0_ENCOUNTER`: `4_Q_B6_CHART-ACTION`
  ) 

##### JOIN STUDY-LEVEL DATA 
df_data <- dplyr::left_join(df_data, df_studies, by="ID.Study") 

#SET IDS AND ASSIGNED BLOCK TO HANDLE PROLIFIC AND TUMBLR 
df_data <- df_data %>% 
  mutate(
    # SET ASSIGNMENT BLOCK FOR TUMBLR
    Assigned.Block = if_else( (Distribution =="TUMBLR"), RANDOM_BLOCK, Assigned.Block),
    Assigned.Block = factor(Assigned.Block), 
    # PID = if_else( (Distribution =="TUMBLR"), ID.Qualtrics, ID.Prolific),
    PID = factor(ID.Qualtrics)
  ) %>% #DROP RANDOM_BLOC COLUMNS
  select (-RANDOM_BLOCK) %>% 
  select(
    #REORDER
    PID, 
    Distribution, 
    Assigned.Block,
    ID.Qualtrics:ID.Session,
    EndState,
    StartDate: Q_RelevantIDLastStartDate, 
    Prolific.Name, Qualtrics.Survey,  Qualtrics.URL, Description, Sample, Scope, 
    duration.sec:PLATFORM,
    `0_Q_B0_ENCOUNTER` : `4_Q_B6_CHART-ACTION`
  )


#2B CLEAN MASTER PARTICIPANT-LEVEL DF  #########################################
#### SEGREGATE PARTICIPANTS WHO DID NOT COMPLETE ###############################
## [1 row / qualtrics submission] ################
## NOTE it is common for prolific participants to fail the 
## screening verification, and then try again but change their 
## screening verification answers (ie. one prolific ID for multiple qualrics IDs)
df_exclude <- df_data %>% 
  filter( 
    !is.na(TerminateFlag) | Finished == FALSE | EndState != "COMPLETE"  
  ) %>% 
  select(
    PID, Distribution, ID.Qualtrics, ID.Prolific, ID.Study, Assigned.Block, Scope, Source, Progress, 
    Finished, TerminateFlag, EndState,StartDate, duration.min, 
    D_gender:D_politicsFiscal, SCREEN_workMethod:FEEDBACK, Prolific.Name:Scope
  )  %>% 
  mutate(
    EndState = if_else( (Progress < 100), "abandoned", EndState),
    EndState = if_else( (str_detect(EndState,"screen")), "screened", EndState),
    EndState = if_else( (TerminateFlag == "Screened" & is.na(EndState)), "screened", EndState),
    EndState = factor(EndState)
  )

#### MASTER VALID DATA [WIDE] 1 row per qualtrics entry #########################
## [1 row / qualtrics submission] ################
df_data <- df_data %>% filter(
  PID %nin% df_exclude$PID
  # (Finished == TRUE ) & is.na(TerminateFlag)
) %>% mutate(
  EndState = droplevels(EndState),
  ID.Prolific = droplevels(ID.Prolific),
  ID.Qualtrics = droplevels(ID.Qualtrics), 
  PID = droplevels((PID))
  
)

#sanity check === SHOULD BE O
#no qualtrics surveys in good data that weren't finished
print("Number of PID entries in df_data AND nofinish/excluded? [should be 0]")

## [1] "Number of PID entries in df_data AND nofinish/excluded? [should be 0]"

sum(df_data$PID %in% df_exclude$PID)

## [1] 0

#sanity check === SHOULD BE O
#no duplicated PROLIFIC ids in good data 
print("Number of PIDs duplicated in df_data print [should be 0]")

## [1] "Number of PIDs duplicated in df_data print [should be 0]"

sum(duplicated(df_data$PID))

## [1] 0

## save participant-level data file for qda validation 
## note that this DOES contain pilot data 
## this does NOT contain excluded participants 
write.csv(df_data, file = "data/output/df_participants.csv", na="")


#3 CREATE TRIAL LEVEL DFS FOR QDA ###############################################
#### CHART LEVEL DATA FRAME (LONG) FOR QDA (incl demographics) ##################
#### INCLUDES PILOT DATA FROM DATACOLLAR BLUECOLLAR PROLIFIC RECRUITMENT ########
# 1 ROW / participant X GRAPH including demographics 
# UNRAVEL TO QUESTIONS
df_qda_input <- df_data %>% 
  # select(
  # ID.Qualtrics:ID.Study, PLATFORM,
  # contains("_Q_"), contains("loop")
# ) %>% 
  pivot_longer( #PIVOT ON stimulus
  cols = contains("_Q_"),
  names_to = c("stimulus","dummy","BLOCK","QUESTION"),
  values_to = c("value"),
  names_sep = "_"
) %>% select(-dummy) %>% 
  unite(
   BLOCK:stimulus, col="STIMULUS", sep="-", remove=FALSE
) %>% 
  mutate(
    BLOCK = factor(BLOCK),
    STIMULUS = factor(STIMULUS),
    QUESTION = str_replace_all(QUESTION,"-","_"),
    QUESTION = factor(QUESTION),
    STIMULUS_CATEGORY = str_remove(STIMULUS,"B.-"),
    STIMULUS_CATEGORY = factor(STIMULUS_CATEGORY,
                  levels=c("0","4","3","2","1"),
                  labels= c("F","D","C","B","A"))
) %>% 
  select(-stimulus) %>% 
# RE-RAVEL UP TO STIMULI
  filter(!is.na(value)) %>% 
   pivot_wider(
    names_from = QUESTION,
    values_from = value 
  ) %>%  
  tidyr::unnest() %>%  # handle r coerces values to lists
  mutate(
    across(contains("MAKER_ID") | contains("MAKER_GENDER") | contains("MAKER_AGE"), factor),
    across(contains("_CONF") | contains("_LATENCY"), as.numeric),
    across(MAKER_DESIGN:MAKER_TRUST, as.numeric),
    across(CHART_LIKE:CHART_TRUST, as.numeric),
    ENCOUNTER = factor(ENCOUNTER),
    # loop_number = as.numeric(loop_number),
    # loop_number = ifelse(is.na(loop_number), 0, loop_number),
    MAKER_LATENCY = round(MAKER_LATENCY/60,2), #CHANGE TO MINS
    CHART_LATENCY = round(CHART_LATENCY/60,2) #CHANGE TO MINS
  )

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c()`.

#WRITE A CSV FILE AS THE BASIS FOR THE QUALITATIVE DATA ANALYSIS
write.csv(df_qda_input, file = "data/output/df_qda_input.csv", na="")


#### REMOVE PILOT DATA FROM DF_DATA 
## pilot data IS not included QDA file, but not in quant analysis 
df_data <- df_data %>% 
  filter(Scope != "pilot") %>% 
  mutate(
    ID.Study = droplevels(ID.Study), 
    ID.Prolific = droplevels(ID.Prolific),
    ID.Qualtrics = droplevels(ID.Qualtrics),
    PID = droplevels(PID)
  ) 

########## CHECK BLOCK COUNTS 
# title = "Participants by Condition and Data Collection Modality"
# cols = c("Control Condition","Impasse Condition","Total for Period")


title = "Number of abandoned/screened/rejected attempts "
cols = c("Block","pilot","study2","sum")
cont <- table(df_exclude$Assigned.Block, df_exclude$Scope)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Number of abandoned/screened/rejected attempts

Block	pilot	study2	sum
1	107	30	137
2	0	30	30
3	0	34	34
4	0	26	26
5	0	15	15
6	0	23	23
Sum	107	158	265

title = "Number of abandoned/screened/rejected attempts by type"
cols = c("Rejection Type", "Sum")
cont <- table(df_exclude$EndState)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Number of abandoned/screened/rejected attempts by type

Rejection Type	Sum
abandoned	206
didnot-follow-instructions	25
failed_nonconsent	4
illegible-english	2
low-effort	13
low-quality	1
screened	52
Sum	303

title = "Number of successful surveys"
cols = c("Assigned.Block", "Sum")
cont <- table(df_data$Assigned.Block)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Number of successful surveys

Assigned.Block	Sum
1	52
2	52
3	51
4	51
5	50
6	50
Sum	306

title = "Number of successful surveys by distribution"
cols = c("Sampling Platform", "Sum")
cont <- table(df_data$Distribution)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Number of successful surveys by distribution

Sampling Platform	Sum
PROLIFIC	240
TUMBLR	66
Sum	306

title = "Number of successful surveys by distribution and block"
cols = c("Sampling Platform", "Block-1","Block-2","Block-3","Block-4","Block-5","Block-6", "Sum")
cont <- table(df_data$Distribution, df_data$Assigned.Block)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Number of successful surveys by distribution and block

Sampling Platform	Block-1	Block-2	Block-3	Block-4	Block-5	Block-6	Sum
PROLIFIC	40	40	40	40	40	40	240
TUMBLR	12	12	11	11	10	10	66
Sum	52	52	51	51	50	50	306

# END WRANGLE MASTER WIDE PARTICIPANT LEVEL DATA FRAME 
################################################################################################
################################################################################################

print("df_data represents full set of valid complete participants")

## [1] "df_data represents full set of valid complete participants"

1.0.4 Wrangle Question and Trial Level

#4 CREATE QUESTION LEVEL DFS ###################################################################
#### QUESTION LEVEL DATA FRAME (LONG) ##########################
# unravel ALL the way down to questions 
# 1 row per participant-graph-question
df_questions <- df_data %>% 
  select(
  PID, duration.min,  Assigned.Block,
  Sample, Scope, Distribution, PLATFORM, 
  D_gender:D_politicsFiscal, 
  contains("_Q_"), contains("loop"), 
) %>% 
  pivot_longer( #PIVOT ON stimulus
  cols = contains("_Q_"),
  names_to = c("stimulus","dummy","BLOCK","QUESTION"),
  values_to = c("value"),
  names_sep = "_"
) %>% select(-dummy) %>% 
  unite(
   BLOCK:stimulus, col="STIMULUS", sep="-", remove=FALSE
) %>% 
  mutate(
    BLOCK = factor(BLOCK),
    STIMULUS = factor(STIMULUS),
    QUESTION = str_replace_all(QUESTION,"-","_"),
    QUESTION = factor(QUESTION),
    STIMULUS_CATEGORY = str_remove(STIMULUS,"B.-"),
    STIMULUS_CATEGORY = factor(STIMULUS_CATEGORY,
                  levels=c("0","4","3","2","1"),
                  labels= c("F","D","C","B","A"))
) %>% 
  select(-stimulus) %>% filter(!is.na(value))


#### SD QUESTION LEVEL DATA FRAME (LONG) ##########################
# ravel up one level from questions
# 1 row per participant-question with all blocks as cols for SD qs
df_sd_questions <- df_questions %>%
  select(-BLOCK,-STIMULUS_CATEGORY) %>% #drop block in order to work at stimulus level
  filter(QUESTION %in% ref_sd_questions) %>% 
  pivot_wider(
    names_from = STIMULUS,
    values_from = value 
  ) %>%  
  tidyr::unnest() %>%  # handle r coerces values to lists
  mutate(
    across(contains("-") , as.numeric),
    QUESTION = droplevels(QUESTION)
  )

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c()`.

#5 SPECIAL DFS FOR MULTISELECT QS  #####################################################
#### MULTI-SELECT QUESTIONS  ##########################

## DF_TOOLS 
# 1 ROW per PARTICIPANT X STIMULUS X tool_id selection 
# tool_id is a multiselect field 
df_tools <- df_questions %>% 
  # select(ID.Prolific, QUESTION, value) %>%
  filter(QUESTION  %in% c("TOOL_ID", "TOOL_CONF")) %>% 
  mutate(QUESTION = fct_drop(QUESTION)) %>% 
  pivot_wider(
    names_from = QUESTION,
    values_from = value 
  ) %>% 
  separate_longer_delim(
    cols = TOOL_ID,
    delim = ","
  ) %>% 
  mutate(TOOL_ID = factor(TOOL_ID, 
                             levels = c("?", "design_basic","design_advanced", "viz_basic", "viz_advanced", "programming")),
         PID = droplevels(PID),
         TOOL_CONF = as.numeric(TOOL_CONF)
         ) 

## DF_ACTIONS 
# 1 ROW per PARTICIPANT X STIMULUS X chart_action selection 
# chart_action is a multiselect field 
df_actions <- df_questions %>% 
  filter(QUESTION == "CHART_ACTION") %>% 
  mutate(QUESTION = fct_drop(QUESTION)) %>% 
  separate_longer_delim(
    cols = value,
    delim = ","
  ) %>% 
  mutate(CHART_ACTION = factor(value, 
                                  levels =  c("NOTHING — just keep scrolling",
                                              "unfollow / block the source",
                                              "post a comment",
                                              "share / repost",
                                              "share / repost WITH comment",
                                              "look up more information about the topic or source"),
                                  labels =  c("nothing",
                                              "unfollow/block",
                                              "comment",  
                                              "share",
                                              "share w/ comment",
                                              "seek information")),
         PID = droplevels(PID)) %>% 
  select(-value,-QUESTION)


#6 DFS FOR TRIAL LEVEL ANALYSIS   ######################################################
#### CHART LEVEL DATA FRAME (LONG) #####################################################
# roll partway back up from questions 
# 1 row per participant X graph 
# unnest https://stackoverflow.com/questions/58035452/pivot-wider-outputs-s3-vctrs-list-of-objects
df_graphs_full <- df_questions %>% 
  pivot_wider(
    names_from = QUESTION,
    values_from = value 
  ) %>%  
  tidyr::unnest() %>%  # handle r coerces values to lists
  mutate(
    across(contains("MAKER_ID") | contains("MAKER_GENDER") | contains("MAKER_AGE"), factor),
    across(contains("_CONF") | contains("_LATENCY"), as.numeric),
    across(MAKER_DESIGN:MAKER_TRUST, as.numeric),
    across(CHART_LIKE:CHART_TRUST, as.numeric),
    ENCOUNTER = factor(ENCOUNTER),
    # loop_number = as.numeric(loop_number),
    # loop_number = ifelse(is.na(loop_number), 0, loop_number),
    MAKER_LATENCY = round(MAKER_LATENCY/60,2), #CHANGE TO MINS
    CHART_LATENCY = round(CHART_LATENCY/60,2), #CHANGE TO MINS
    MAKER_ID = factor( MAKER_ID,levels = c("business", "political", "education","news","organization","individual"))
  )

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c()`.

#7 DFS FOR TRIAL LEVEL ANALYSIS w/o free response ######################################################
#### CHART LEVEL DATA FRAME (LONG) ##########################
## SUBSET OF COLUMNS EXCLUDING THE FREE RESPONSES and MULTISELECT
df_graphs <- df_graphs_full %>% 
  select( !where(is.character))

print("df_graphs_full is trial level dataset; df_graphs is trial level quantitative data only")

## [1] "df_graphs_full is trial level dataset; df_graphs is trial level quantitative data only"

1.0.5 TODO Wrangle QDA

#8 TRIAL LEVEL DATA INCLUDING QDA CODING ############################################################
#### CHART LEVEL DATA FRAME (LONG) ##########################




# ### TODO PICK UP HERE
# 
# 
# # import coded QDA data
# # participant X chart level
# df_coded <- read_csv("data/input/QDA/s2_qda_maker.csv", col_names = TRUE)
# # create unique key for join
# df_coded <- df_coded %>% filter(
#   Scope != "pilot"   #filter out pilot data
# ) %>% mutate(
#   TRIAL = paste0(STIMULUS,"_",ID.Prolific)
# ) %>% select(
#   #TODO ADD CODED DATA THEY ARE ADDED
#   TRIAL, CODE_M_ID_SPECIFIC
# )
# 
# # create base dataframe
# df_graphs_coded <- df_graphs_full %>% mutate(
#   TRIAL = paste0(STIMULUS,"_",ID.Prolific)
# )
# 
# ##SANITY CHECK
# print("coded df & trials df have same num rows")
# nrow(df_coded) == nrow(df_graphs_coded)
# 
# df_graphs_coded <- dplyr::left_join(df_graphs_coded, df_coded, by="TRIAL") %>%
#   select(
#   #reorder cols
#   duration.min:D_politicsFiscal, BLOCK:MAKER_ID, MAKER_CONF:CHART_ACTION, STIMULUS, ID.Prolific,  TRIAL, MAKER_DETAIL, CODE_M_ID_SPECIFIC
# )
# 
# ##SANITY CHECK
# print("coded df & trials df have same num rows")
# nrow(df_graphs) == nrow(df_graphs_coded)
# 
# #####################################################################################
# ################ SANITY CHECKS ######################################################
# #CHECK BLOCKS PER PARTICIPANT
# # every participant should have 2 blocks
# check_subject_blocks <- df_questions %>%
#   group_by(ID.Prolific) %>% summarise(
#     n_block = length(unique(BLOCK))
#   )
# print("every participant should have two blocks, [block 0 + randomly assigned block]")
# all(check_subject_blocks$n_block==2)
# 
# 
# #CHECK STIMULI PER PARTICIPANT
# # every participant should have 5 graphs
# check_subject_graphs <- df_questions %>%
#   group_by(ID.Prolific) %>%summarise(
#     n_graphs = length(unique(STIMULUS))
#   ) #each participant should have five graphs
# print("every participant should have five stimuli [B0 + 4 stimuli in a block]]")
# all(check_subject_graphs$n_graphs==5)
# 
# 
# 
# 
# 
# 
# 
# 
# #CLEANUP
# rm(check_subject_blocks, check_subject_graphs, df_graphs_qda, df_coded)

1.1 Graphing Functions

############## RETURNS SD STACKED AND COLORED BY BY X
## LOOP STYLE
multi_sd <- function (df, left, right, x, y, color) {

  # g <- ggplot(df, aes(y = .data[[x]], x = {{y}}, color = {{color}}))+
  g <- ggplot(df, aes(y = .data[[x]], x = .data[[y]], color = .data[[color]]))+
  geom_boxplot(width = 0.5) +
  geom_jitter(width = 0.1, alpha=0.5) +
  scale_y_continuous(limits=c(-1,101)) +
  labs(x="", y="") +
  coord_flip() +
  guides(
    y = guide_axis_manual(labels = left),
    y.sec = guide_axis_manual(labels = right)
  ) + theme_minimal()

  return(g)
}


############## RETURNS SINGLE SD 
## LOOP STYLE
single_sd <- function (df, left, right, x) {

  g <- ggplot(df, aes(y = {{x}}, x = ""))+
  geom_boxplot(width = 0.5) +
  geom_jitter(width = 0.1, alpha=0.5) +
  scale_y_continuous(limits=c(-1,101)) +
  labs(x="", y="") +
  coord_flip() +
  guides(
    y = guide_axis_manual(labels = left),
    y.sec = guide_axis_manual(labels = right)
  ) + theme_minimal()

  return(g)
}



######### RETURNS SINGLE SD
##  APPLY STYLE
plot_sd = function (data, column, type, split, boxplot) {
    
  ggplot(df, aes(y = .data[[column]], x="")) +
    {if(boxplot) geom_boxplot(width = 0.5) } +
    geom_jitter(width = 0.1, alpha=0.3, {if(split) aes(color=Distribution)}) + 
    {if(split) facet_grid(Distribution ~ .)} +
    scale_y_continuous(limits=c(-1,101)) +
    labs(x="", y="") +
    coord_flip()  +
    {if(type == "S")
      guides(
        y = guide_axis_manual(labels = ref_labels[column,"left"]),
        y.sec = guide_axis_manual(labels = ref_labels[column,"right"])
      )} +  
    {if(type == "Q")
      guides(
        y = guide_axis_manual(labels = ref_labels[q,"left"]),
        y.sec = guide_axis_manual(labels = ref_labels[q,"right"])
      )} +  
  theme_minimal()  + 
     labs (
       caption = column
     )
}

2 PROFILING

2.1 Descriptives

dfSummary(df_data %>% select(PID, duration.min, Distribution, Assigned.Block, PLATFORM, 
                             contains("D_"), Prolific.Name:Scope, contains("SCREEN_")), 
          headings = TRUE,
          plain.ascii  = FALSE,
          style        = 'grid',
          graph.magnif = 0.85,
          varnumbers = FALSE,
          valid.col    = FALSE,
          tmp.img.dir  = "/tmp")

2.1.1 Data Frame Summary

2.1.1.1

Dimensions: 306 x 29
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
PID [factor]	1. R_11XUYk3OpC3HJPM 2. R_133GgyRbPsqfxPD 3. R_1A0uAWls6AhmTfr 4. R_1aUA7MBz4XYLbkl 5. R_1azgETg5gjYGzUf 6. R_1Bc6WwJjiC9VBVn 7. R_1Cq1wvlMciQUHh8 8. R_1D2krAejiiSE3Ln 9. R_1dz1Y8zwrsbEbB6 10. R_1EnkVRz2u8SWbla [ 296 others ]	1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 1 ( 0.3%) 296 (96.7%)		0 (0.0%)
duration.min [numeric]	Mean (sd) : 44.7 (26) min < med < max: 10.9 < 38.1 < 227.6 IQR (CV) : 25.9 (0.6)	294 distinct values		0 (0.0%)
Distribution [factor]	1. PROLIFIC 2. TUMBLR	240 (78.4%) 66 (21.6%)		0 (0.0%)
Assigned.Block [factor]	1. 1 2. 2 3. 3 4. 4 5. 5 6. 6	52 (17.0%) 52 (17.0%) 51 (16.7%) 51 (16.7%) 50 (16.3%) 50 (16.3%)		0 (0.0%)
PLATFORM [factor]	1. Facebook 2. Instagram 3. LinkedIn 4. Tumblr 5. Twitter/X	72 (23.5%) 93 (30.4%) 8 ( 2.6%) 64 (20.9%) 69 (22.5%)		0 (0.0%)
D_gender [factor]	1. Female 2. Male 3. Non-binary / third gender 4. Prefer not to say 5. Prefer to self-describe	152 (49.7%) 106 (34.6%) 32 (10.5%) 3 ( 1.0%) 13 ( 4.2%)		0 (0.0%)
D_gender_4_TEXT [character]	1. genderqueer 2. Agender 3. gender fluid 4. genderqueer (dislike term 5. genderqueer trans man 6. Genderqueer wlw 7. genderqueer woman 8. queer 9. She/They 10. Trans male [ 2 others ]	2 (15.4%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 1 ( 7.7%) 2 (15.4%)		293 (95.8%)
D_race [character]	1. White or Caucasian 2. Asian 3. Black or African American 4. Other 5. White or Caucasian,Asian 6. White or Caucasian,Black 7. White or Caucasian,Other 8. Prefer not to say 9. White or Caucasian,Americ 10. American Indian/Native Am [ 4 others ]	201 (65.7%) 34 (11.1%) 34 (11.1%) 11 ( 3.6%) 5 ( 1.6%) 4 ( 1.3%) 4 ( 1.3%) 3 ( 1.0%) 3 ( 1.0%) 2 ( 0.7%) 5 ( 1.6%)		0 (0.0%)
D_education [factor]	1. NA 2. some high school or less 3. high school diploma or GE 4. some college 5. associates or technical d 6. undergradudate degree 7. graduate or professional	0 ( 0.0%) 4 ( 1.3%) 28 ( 9.2%) 61 (19.9%) 32 (10.5%) 130 (42.5%) 51 (16.7%)		0 (0.0%)
D_employmentStatus [factor]	1. A homemaker or stay-at-ho 2. Other 3. Retired 4. Student 5. Unemployed and looking fo 6. Working full-time 7. Working part-time	12 ( 3.9%) 10 ( 3.3%) 4 ( 1.3%) 28 ( 9.2%) 49 (16.0%) 152 (49.7%) 51 (16.7%)		0 (0.0%)
D_income [factor]	1. Prefer not to say 2. Less than $25,000 3. $25,000-$49,999 4. $50,000-$74,999 5. $75,000-$99,999 6. $100,000-$149,999 7. $150,000 or more	14 ( 4.6%) 55 (18.0%) 63 (20.6%) 70 (22.9%) 36 (11.8%) 46 (15.0%) 22 ( 7.2%)		0 (0.0%)
D_work_detail [character]	1. Unemployed 2. N/A 3. Student 4. homemaker 5. IT 6. Manager 7. Artist 8. Cashier 9. Disabled 10. engineer [ 262 others ]	9 ( 3.0%) 5 ( 1.6%) 5 ( 1.6%) 3 ( 1.0%) 3 ( 1.0%) 3 ( 1.0%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 269 (88.2%)		1 (0.3%)
D_zipcode [character]	1. 30019 2. 11105 3. 20149 4. 27858 5. 28645 6. 37917 7. 60615 8. 77062 9. 85210 10. 95123 [ 284 others ]	3 ( 1.0%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 2 ( 0.7%) 284 (93.1%)		1 (0.3%)
D_age [factor]	1. 18-24 2. 25-34 3. 35-44 4. 45-54 5. 55-64 6. 65+ years	64 (20.9%) 125 (40.8%) 56 (18.3%) 45 (14.7%) 13 ( 4.2%) 3 ( 1.0%)		0 (0.0%)
D_politicalParty [factor]	1. No preference 2. Other 3. Independent 4. Republican 5. Democrat	12 ( 3.9%) 26 ( 8.5%) 84 (27.5%) 27 ( 8.8%) 157 (51.3%)		0 (0.0%)
D_politicalParty_OTHER [character]	1. Socialist 2. leftist 3. Progressive 4. socialist 5. A progressive who must vo 6. Anarchist 7. Freedom and peace party 8. Green 9. I don’t like any 10. I think I hate the govern [ 9 others ]	4 (15.4%) 3 (11.5%) 2 ( 7.7%) 2 ( 7.7%) 1 ( 3.8%) 1 ( 3.8%) 1 ( 3.8%) 1 ( 3.8%) 1 ( 3.8%) 1 ( 3.8%) 9 (34.6%)		280 (91.5%)
D_politicsSocial [numeric]	Mean (sd) : 27.4 (26.6) min < med < max: 0 < 22 < 100 IQR (CV) : 43 (1)	64 distinct values		0 (0.0%)
D_politicsFiscal [numeric]	Mean (sd) : 34.5 (28.5) min < med < max: 0 < 34 < 100 IQR (CV) : 42.8 (0.8)	76 distinct values		0 (0.0%)
Prolific.Name [factor]	1. MAG_S2_PROLIFIC_BLUECOLLA 2. MAG_S2_PROLIFIC_DATACOLLA 3. MAG_S2_PROLIFIC_GENERAL_B 4. MAG_S2_PROLIFIC_GENERAL_B 5. MAG_S2_PROLIFIC_GENERAL_B 6. MAG_S2_PROLIFIC_GENERAL_B 7. MAG_S2_PROLIFIC_GENERAL_B 8. MAG_S2_PROLIFIC_GENERAL_B 9. MAG_S2_PROLIFIC_GENERAL_B 10. MAG_S2_PROLIFIC_GENERAL_B [ 4 others ]	0 ( 0.0%) 0 ( 0.0%) 40 (13.1%) 20 ( 6.5%) 20 ( 6.5%) 18 ( 5.9%) 22 ( 7.2%) 21 ( 6.9%) 19 ( 6.2%) 19 ( 6.2%) 127 (41.5%)		0 (0.0%)
Qualtrics.Survey [factor]	1. MAG_S2_PROLIFIC_GENERAL_1 2. MAG_S2_PROLIFIC_GENERAL_2 3. MAG_S2_PROLIFIC_GENERAL_3 4. MAG_S2_PROLIFIC_GENERAL_4 5. MAG_S2_PROLIFIC_GENERAL_5 6. MAG_S2_PROLIFIC_GENERAL_6 7. MAG_S2_PROLIFIC-BLUECOLLA 8. MAG_S2_PROLIFIC-DATACOLLA 9. MAG_S2_TUMBLR_FREE 10. MAG_S2_TUMBLR_PAID	40 (13.1%) 40 (13.1%) 40 (13.1%) 40 (13.1%) 40 (13.1%) 40 (13.1%) 0 ( 0.0%) 0 ( 0.0%) 5 ( 1.6%) 61 (19.9%)		0 (0.0%)
Qualtrics.URL [factor]	1. https://mit.co1.qualtrics\ 2. https://mit.co1.qualtrics\ 3. https://mit.co1.qualtrics\ 4. https://mit.co1.qualtrics\ 5. https://mit.co1.qualtrics\ 6. https://mit.co1.qualtrics\ 7. https://mit.co1.qualtrics\ 8. https://mit.co1.qualtrics\ 9. https://mit.co1.qualtrics\ 10. https://mit.co1.qualtrics	40 (13.1%) 61 (19.9%) 40 (13.1%) 5 ( 1.6%) 40 (13.1%) 40 (13.1%) 0 ( 0.0%) 0 ( 0.0%) 40 (13.1%) 40 (13.1%)		0 (0.0%)
Description [character]	1. TUMBLR-paid 2. block1-full 3. block3-fill 4. block4-20 5. block5-fill 6. block6-fill 7. block2-10 8. block2-fill 9. block4-fill 10. block5-20 [ 3 others ]	61 (19.9%) 40 (13.1%) 22 ( 7.2%) 21 ( 6.9%) 21 ( 6.9%) 21 ( 6.9%) 20 ( 6.5%) 20 ( 6.5%) 19 ( 6.2%) 19 ( 6.2%) 42 (13.7%)		0 (0.0%)
Sample [factor]	1. blue-collar 2. data-collar 3. general-prolific 4. tumblr-free 5. tumblr-paid	0 ( 0.0%) 0 ( 0.0%) 240 (78.4%) 5 ( 1.6%) 61 (19.9%)		0 (0.0%)
Scope [factor]	1. pilot 2. study2	0 ( 0.0%) 306 (100.0%)		0 (0.0%)
SCREEN_workMethod [character]	1. btwn-50-75 2. btwn25-50 3. less-25 4. more-75	49 (16.0%) 16 ( 5.2%) 42 (13.7%) 199 (65.0%)		0 (0.0%)
SCREEN_workFunction [character]	1. other 2. Operations 3. Education-Professional 4. IT 5. Research 6. Healthcare-Professional 7. Administration-PersonalAs 8. Design-Creative 9. Sales-Business-Developmen 10. Data-Analysis [ 64 others ]	71 (23.2%) 23 ( 7.5%) 20 ( 6.5%) 17 ( 5.6%) 17 ( 5.6%) 15 ( 4.9%) 14 ( 4.6%) 11 ( 3.6%) 11 ( 3.6%) 10 ( 3.3%) 97 (31.7%)		0 (0.0%)
SCREEN_workFunction_TEXT [character]	1. N/A 2. Student 3. Unemployed 4. n/a 5. unemployed 6. Logistics 7. Not applicable 8. Alternative Health 9. Business services 10. Business support [ 44 others ]	7 ( 9.6%) 5 ( 6.8%) 4 ( 5.5%) 3 ( 4.1%) 3 ( 4.1%) 2 ( 2.7%) 2 ( 2.7%) 1 ( 1.4%) 1 ( 1.4%) 1 ( 1.4%) 44 (60.3%)		233 (76.1%)
SCREEN_socialMedia [character]	1. Twitter,Reddit,LinkedIn 2. Facebook,Instagram,Reddit 3. Facebook,Twitter,Instagra 4. Facebook,Twitter,Youtube, 5. Facebook,Instagram,Reddit 6. Facebook,Twitter,Youtube, 7. Facebook,Twitter,Reddit,L 8. Facebook,Instagram 9. Facebook,LinkedIn 10. Facebook,Twitter,Instagra [ 178 others ]	8 ( 2.6%) 7 ( 2.3%) 7 ( 2.3%) 7 ( 2.3%) 6 ( 2.0%) 6 ( 2.0%) 5 ( 1.6%) 4 ( 1.3%) 4 ( 1.3%) 4 ( 1.3%) 248 (81.0%)		0 (0.0%)
SCREEN_socialMedia_TEXT [character]	1. Discord 2. Bluesky 3. 4chan 4. alt tech, to avoid exactl 5. discord 6. mastedons,telegram 7. Monster, GlassDoor 8. nextdoor 9. Pinterest 10. Plurk, Slack [ 3 others ]	4 (23.5%) 2 (11.8%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 1 ( 5.9%) 3 (17.6%)		289 (94.4%)

2.2 Demographics

2.2.1 Total Response Time

df <- df_data

## BOXPLOT — SURVEY RESPONSE TIME 
ggplot(df_data, aes(x=fct_rev(Assigned.Block), y=duration.min, color=Assigned.Block))+
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(), alpha = 0.3) +
  facet_grid(Distribution ~.) + 
  coord_flip() + 
  labs( y = "Survey Response Time (mins)", x="",
        title = "TOTAL Response Time by Sample",
        subtitle = "(distributions of response times are similar across samples, as expected)") +
  theme_minimal() + theme(legend.position = "none") 

## RIDGEPLOT — SURVEY RESPONSE TIME 
ggplot(df, aes(x = duration.min, y = fct_rev(Assigned.Block), fill = fct_rev(Assigned.Block))) +
  geom_density_ridges(scale=0.8) +
  # geom_boxplot()+
  stat_pointinterval()+
  theme_ridges() +
  scale_fill_discrete(direction=-1)+
  facet_grid(Distribution ~.) + 
  theme_minimal() + 
  theme(legend.position = "none") +
    labs( x = "Survey Response Time (mins)", y="",
        title = "TOTAL Response Time by Sample",
        subtitle = "(distributions of response times are similar across samples, as expected)") 

## Picking joint bandwidth of 7.37

## Picking joint bandwidth of 13.8

desc.duration <- psych::describe(df_data$duration.min)

## TODO TUMBLR AS WELL 

Participant response times ranged from 10.88 to 227.57 minutes, with a mean response time of 44.71 minutes, SD = 25.98.

2.2.2 Maker Page Response Time

df <- df_graphs

## BOXPLOT — MAKER RESPONSE TIME  
ggplot(df, aes(x=STIMULUS_CATEGORY, y=MAKER_LATENCY, color=STIMULUS_CATEGORY))+
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(jitter.width=1.5), alpha = 0.3) +
  facet_grid(Distribution ~ .)+
  scale_color_viridis(discrete=TRUE, option="viridis") + 
  coord_flip() + 
  labs( y = "MAKER Page Response Time (mins)", x="",
        title = "MAKER Page Response Time by Stimulus Category",
        subtitle = "(distributions of response times are similar across samples, as expected)") + 
  theme_minimal() + theme(legend.position = "none") 

## RIDGEPLOT — MAKER PAGE RESPONSE
ggplot(df, aes(x = MAKER_LATENCY, y = STIMULUS_CATEGORY, fill = STIMULUS_CATEGORY)) +
  geom_density_ridges(scale=0.8) +
  # geom_boxplot()+
  stat_pointinterval()+
  # theme_ridges() +
  facet_grid(Distribution ~ .) + 
  scale_fill_viridis(discrete=TRUE, option="viridis") + 
  theme_minimal() + 
  theme(legend.position = "none") +
    labs( x = "MAKER Page Response Time (mins)", y="",
        title = "MAKER Page Response Time by Stimulus Category",
        subtitle = "(distributions of response times are similar across samples, as expected)") 

## Picking joint bandwidth of 0.383

## Picking joint bandwidth of 0.897

Maker page response times ranged from 0.37 to 39.52 minutes, with a mean response time of 3 minutes.

2.2.3 Chart Page Response Time

df <- df_graphs

## BOXPLOT — MAKER RESPONSE TIME  
ggplot(df, aes(x=STIMULUS_CATEGORY, y=CHART_LATENCY, color=STIMULUS_CATEGORY))+
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(jitter.width=1.5), alpha = 0.3) +
  scale_color_viridis(discrete=TRUE, option="viridis") + 
  facet_grid(Distribution ~ .) + 
  coord_flip() + 
  labs( y = "CHART Page Response Time (mins)", x="",
        title = "CHART Page Response Time by Stimulus Category",
        subtitle = "(distributions of response times are similar across samples, as expected)") +
  theme_minimal() + theme(legend.position = "none") 

## RIDGEPLOT — MAKER PAGE RESPONSE
ggplot(df, aes(x = CHART_LATENCY, y = STIMULUS_CATEGORY, fill = STIMULUS_CATEGORY)) +
  geom_density_ridges(scale=0.8) +
  # geom_boxplot()+
  stat_pointinterval()+
  # theme_ridges() +
  scale_fill_viridis(discrete=TRUE, option="viridis") + 
  facet_grid(Distribution ~ .) + 
  theme_minimal() + 
  theme(legend.position = "none") +
    labs( x = "CHART Page Response Time (mins)", y="",
        title = "CHART Page Response Time by Stimulus Category",
        subtitle = "(distributions of response times are similar across samples, as expected)") 

## Picking joint bandwidth of 0.246

## Picking joint bandwidth of 0.36

Chart page response times ranged from 0.19 to 20.21 minutes, with a mean response time of 1.7 minutes.

2.2.4 Age

# AGE by SAMPLE
ggplot(data = df_data, aes( x = Assigned.Block, fill = fct_rev(D_age) )) +
  geom_bar(position = "stack") +
  facet_grid(Distribution ~ .) + 
  labs( title = "AGE by Sample", subtitle = "Expect similiar across samples", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
  theme_minimal() +
  easy_add_legend_title("")

# OVERALL AGE
ggstatsplot::ggbarstats(df_data, x= D_age, y=Distribution) +
    theme_minimal() +
    scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
    easy_add_legend_title("Age") + 
    labs (title = "Participant Age")

2.2.5 Gender

# GENDER by SAMPLE
ggplot(data = df_data, aes( x = Assigned.Block, fill = fct_rev(D_gender) )) +
  geom_bar(position = "stack") +
  facet_grid(Distribution ~ .) + 
  labs( title = "Gender by Sample", subtitle = "Expect similiar across samples", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
  theme_minimal() 

  # easy_add_legend_title("")


# OVERALL AGE
ggstatsplot::ggbarstats(df_data, x= D_gender, y=Distribution) +
    theme_minimal() +
    scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
    easy_add_legend_title("Gender") + 
    labs (title = "Participant Gender")

#PROLIFIC
df.p <- df_data %>% filter(Distribution == "PROLIFIC")
desc.gender.p <- table(df.p$D_gender) %>% prop.table() 
names(desc.gender.p) <- levels(df.p$D_gender)
p_participants <- nrow(df.p)


#TUMBLR
df.t <- df_data %>% filter(Distribution == "TUMBLR")
desc.gender.t <- table(df.t$D_gender) %>% prop.table() 
names(desc.gender.t) <- levels(df.t$D_gender)
t_participants <- nrow(df.t)


title = "Participant Gender — Self Describe"
cols = c("Text","Count")
cont <- table(df_data$D_gender_4_TEXT)
cont %>% addmargins() %>% kbl(caption = title, col.names = cols) %>%  kable_classic()

Participant Gender — Self Describe

Text	Count
Agender	1
gender fluid	1
genderqueer	2
genderqueer (dislike term non-binary)	1
genderqueer trans man	1
Genderqueer wlw	1
genderqueer woman	1
queer	1
She/They	1
Trans male	1
Transgender male	1
transmasc	1
Sum	13

240 individuals from Prolific participated in Study 2, ( 54% Female, 42% Male, 3% Non-binary, 1% Other).

66 individuals from Tumblr participated in Study 2, ( 35% Female, 6% Male, 38% Non-binary, 21% Other).

2.2.6 Education

df <- df_data

# EDUCATION by SAMPLE
ggplot(data = df, aes( x = Assigned.Block, fill = D_education )) +
  geom_bar(position = "stack") +
  facet_grid(Distribution ~ .)+
  scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
  labs( title = "EDUCATION by Sample",
        subtitle = "Expect similiar across samples") +
  theme_minimal() +
  easy_add_legend_title("Education")

# INCOME BY EDUCATION
ggstatsplot::ggbarstats(df_data, x= D_education, y=D_income) +
    theme_minimal() +
    scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) +
    labs (title = "INCOME by EDUCATION")

2.2.7 Political Values

df <- df_graphs

# FISCAL POLITICAL SAMPLE

leftside <- rep("left-leaning", length(ref_blocks))
rightside <- rep("right-leaning", length(ref_blocks))
g <- ggplot(df_data, aes(x=fct_rev(Assigned.Block), y=D_politicsFiscal, color=Assigned.Block)) +
  geom_boxplot(position=position_dodge(0.9), width = 0.5)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.3) +
  facet_grid(Distribution ~ .)+
  labs( title = "FISCAL VALUES by Sample",
        subtitle = "(expect similar values across samples)",
         y = "Fiscal Politics", x = "") +
  # easy_add_legend_title("Sample") +
  theme_minimal() +
  coord_flip()
g + guides(
  y = guide_axis_manual(
  breaks = ref_blocks,
  labels = leftside
  ),
  y.sec = guide_axis_manual(
  breaks = ref_blocks,
  labels = rightside
))

# SOCIAL POLITICAL SAMPLE

leftside <- rep("left-leaning", length(ref_blocks))
rightside <- rep("right-leaning", length(ref_blocks))
g <- ggplot(df_data, aes(x=fct_rev(Assigned.Block), y=D_politicsSocial, color=Assigned.Block)) +
  geom_boxplot(position=position_dodge(0.9), width = 0.5)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.3) +
  facet_grid(Distribution ~ .)+
  labs( title = "SOCIAL VALUES by Sample",
        subtitle = "(expect similar values across samples)",
         y = "Social Politics", x = "") +
  # easy_add_legend_title("Sample") +
  theme_minimal() +
  coord_flip()
g + guides(
  y = guide_axis_manual(
  breaks = ref_blocks,
  labels = leftside
  ),
  y.sec = guide_axis_manual(
  breaks = ref_blocks,
  labels = rightside
))

r_politics <- nrow(df_data %>% filter(D_politicsSocial >= 50)) / nrow(df_data)
l_politics <- nrow(df_data %>% filter(D_politicsSocial < 50)) / nrow(df_data)
r_fiscal <- nrow(df_data %>% filter(D_politicsFiscal >= 50)) / nrow(df_data)
l_fiscal <- nrow(df_data %>% filter(D_politicsFiscal < 50)) / nrow(df_data)
 
rm(g)



#PROLIFIC
df.p <- df_data %>% filter(Distribution == "PROLIFIC")
desc.fiscal.p <- psych::describe(df.p$D_politicsFiscal)
desc.social.p <- psych::describe(df.p$D_politicsSocial)


#TUMBLR
df.t <- df_data %>% filter(Distribution == "TUMBLR")
desc.fiscal.t <- psych::describe(df.t$D_politicsFiscal)
desc.social.t <- psych::describe(df.t$D_politicsSocial)

For the 240 participants recruited from Prolific, a spectrum of Social Political values [ranging from 0 (left-leaning) to 100 (right-leaning)] ranged from 0 to 100, with a mean value of 32.27, SD = 27.49. A spectrum of Fiscal Political values [ranging from 0 (left-leaning) to 100 (right-leaning)] ranged from 0 to 100, with a mean value of 39.4, SD = 29.06.

For the 66 participants recruited from Tumblr, a spectrum of Social Political values [ranging from 0 (left-leaning) to 100 (right-leaning)] ranged from 0 to 50, with a mean value of 9.53, SD = 11.59. A spectrum of Fiscal Political values [ranging from 0 (left-leaning) to 100 (right-leaning)] ranged from 0 to 91, with a mean value of 16.52, SD = 17.22.

Overall, 77.12% of respondents identify with left-leaning social values (vs) 22.88% identifying as right-leaning; while 65.36% of respondents reported left-leaning fiscal values (vs) 34.64% identify as right-leaning.

2.2.8 Politcal Affiliation

# POLITICAL PARTY
ggplot(data = df_data, aes( fill = D_politicalParty, x = Assigned.Block )) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  scale_fill_viridis(discrete=TRUE, option="viridis", direction=-1) + 
  labs( title = "POLITICAL PARTY by Sample",
        subtitle = "Expect similiar across samples", x = "") +
  theme_minimal() +
  easy_add_legend_title("")

2.2.9 Platform Choice

#PLATFORM CHOICE
ggplot( df_data, aes( x = Assigned.Block, fill = PLATFORM)) +
  geom_bar(position = "stack") +
  facet_grid(Distribution ~ .) + 
  labs( title = "PLATFORM CHOICE by Sample",
        subtitle = "Expect similiar across samples", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") + 
  easy_add_legend_title("") +
  theme_minimal()

3 ATTRIBUTIONS

3.1 MAKER ID

3.1.1 Maker ID by Stimulus

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = fct_rev(STIMULUS), fill = MAKER_ID)) +
  geom_bar(position = "fill") +
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  labs( title = "MAKER ID by Stimulus (grouped by CATEGORY) ",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  coord_flip()+
  easy_add_legend_title("") +
  theme_minimal()

3.1.2 Maker ID by Category

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = STIMULUS_CATEGORY, fill = MAKER_ID)) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  labs( title = "MAKER ID by Stimulus Category",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  coord_flip()+
  easy_add_legend_title("") +
  theme_minimal()

3.1.3 Maker ID & Confidence by Stimulus

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER ID AND CONFIDENCE ##############
# MAKER_ID by Sample and BEHAVIOR 
a <- ggplot (df, aes( x = fct_rev(STIMULUS), fill = MAKER_ID)) + 
  # geom_bar(width = 0.5, position = position_dodge(0.5)) +
  geom_bar(position = "fill", width = 0.8) + 
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# MAKER_CONFIDENCE by IDENTIFICATION  
b <- ggplot(df, aes( x = fct_rev(STIMULUS), y = MAKER_CONF, color = MAKER_ID)) + 
  geom_boxplot(position=position_dodge(0.9), width = 0.5)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.4) +
  scale_color_viridis(discrete=TRUE, option="viridis") +
  labs (title = "") +
  labs (x = "") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER ID & CONFIDENCE by Stimulus',
  subtitle = '',
  caption = ''))

###################################################
rm(a,b,p)

3.1.4 Maker ID & Confidence

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER ID AND CONFIDENCE ##############
# MAKER_ID by Sample and BEHAVIOR 
a <- ggplot (df, aes( x = fct_rev(STIMULUS_CATEGORY), fill = MAKER_ID)) + 
  geom_bar(position = "fill") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# MAKER_CONFIDENCE by IDENTIFICATION  
b <- ggplot(df, aes( x = fct_rev(STIMULUS_CATEGORY), y = MAKER_CONF, color = MAKER_ID)) + 
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_viridis(discrete=TRUE, option="viridis") +
  # labs (title = "MAKER-ID-CONFIDENCE") +
  labs (x = "STIMULUS CATEGORY") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER ID & CONFIDENCE',
  subtitle = '',
  caption = ''))

rm(a,b,p)

###################################################

3.2 MAKER AGE

3.2.1 Maker Age by Stimulus

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = fct_rev(STIMULUS), fill = MAKER_AGE)) +
  geom_bar(position = "fill") +
  #SCALES & SPACE FREE SUPPRESSES EMPTY ROWS
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  labs( title = "MAKER AGE by Stimulus (grouped by CATEGORY) ",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  coord_flip()+
  # easy_add_legend_title("") +
  theme_minimal()

3.2.2 Maker Age by Category

df <- df_graphs 

#MAKER AGE
ggplot( df, aes( x = STIMULUS_CATEGORY, fill = MAKER_AGE)) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  labs( title = "MAKER AGE by Stimulus Category",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid(rows= vars(D_age)) +
  coord_flip()+
  theme_minimal()

3.2.3 Maker Age & Confidence by Stimulus

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER ID AND CONFIDENCE ##############
# MAKER_ID by Sample and BEHAVIOR 
a <- ggplot (df, aes( x = fct_rev(STIMULUS), fill = MAKER_AGE)) + 
  geom_bar(position = "fill", width = 0.8) +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# MAKER_CONFIDENCE by IDENTIFICATION  
b <- ggplot(df, aes( x = fct_rev(STIMULUS), y = MAKER_CONF, color = MAKER_AGE)) + 
  geom_boxplot(position=position_dodge(0.9), width = 0.5)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_viridis(discrete=TRUE, option="viridis") +
  labs (title = "") +
  labs (x = "STIMULUS CATEGORY") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER AGE & CONFIDENCE by Category',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.2.4 Maker Age & Confidence by Category

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER ID AND CONFIDENCE ##############
# MAKER_AGE by Sample and BEHAVIOR 
a <- ggplot (df, aes( x = fct_rev(STIMULUS_CATEGORY), fill = MAKER_AGE)) + 
  geom_bar(position = "fill") + #dodge
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# MAKER_CONFIDENCE by IDENTIFICATION  
b <- ggplot(df, aes( x = fct_rev(STIMULUS_CATEGORY), y = AGE_CONF, color = MAKER_AGE)) + 
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_viridis(discrete=TRUE, option="viridis") +
  labs (x = "STIMULUS CATEGORY") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER AGE & CONFIDENCE by Category',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.3 MAKER GENDER

3.3.1 Maker Gender by Stimulus

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = fct_rev(STIMULUS), fill = MAKER_GENDER)) +
  geom_bar(position = "fill") +
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  labs( title = "MAKER GENDER by Stimulus (grouped by CATEGORY) ",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  coord_flip()+
  # easy_add_legend_title("") +
  theme_minimal()

### Maker Gender by Category

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = STIMULUS_CATEGORY, fill = MAKER_GENDER)) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .)+
  labs( title = "MAKER GENDER by Stimulus Category",
        subtitle = "", x = "") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid(rows= vars(D_age)) +
  coord_flip()+
  theme_minimal()

3.3.2 Maker Gender & Confidence by Stimulus

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER ID AND CONFIDENCE ##############
# MAKER_GENDER by STIMULUS
a <- ggplot (df, aes( x = fct_rev(STIMULUS), fill = MAKER_GENDER)) + 
  geom_bar(position = "fill", width = 0.8) +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# GENDER_CONFIDENCE by STIMULUS
b <- ggplot(df, aes( x = fct_rev(STIMULUS), y = GENDER_CONF, color = MAKER_GENDER)) + 
  geom_boxplot(position=position_dodge(0.9), width = 0.5)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_viridis(discrete=TRUE, option="viridis") +
  labs (x = "STIMULUS") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER GENDER & CONFIDENCE by Stimulus',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.3.3 Maker Gender & Confidence by Category

#FILTER DATAFRAME
df <- df_graphs 

######### MAKER GENDER AND CONFIDENCE ##############
a <- ggplot (df, aes( x = fct_rev(STIMULUS_CATEGORY), fill = MAKER_GENDER)) + 
  geom_bar(position = "fill") +
  scale_fill_viridis(discrete=TRUE, option="viridis") +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  theme_minimal() 

# GENDER_CONFIDENCE by GENDER  
b <- ggplot(df, aes( x = fct_rev(STIMULUS_CATEGORY), y = GENDER_CONF, color = MAKER_GENDER)) + 
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_viridis(discrete=TRUE, option="viridis") +
  labs (x = "STIMULUS CATEGORY") + 
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'MAKER GENDER & CONFIDENCE by Category',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.4 TOOL CHOICE

3.4.1 Tool ID by Stimulus

df <- df_tools 

# TOOL CHOICE BY STIMULUS
ggplot(data = df, aes( fill = fct_rev(TOOL_ID), x = fct_rev(STIMULUS) )) +
  geom_bar(position = "fill") +
  coord_flip() + 
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  scale_fill_paletteer_d("awtools::a_palette", direction = 1)+
  scale_y_continuous(labels = scales::percent) + 

  labs( title = "TOOL ID by Stimulus (grouped by Category)",
        subtitle = "", x = "") +
  easy_add_legend_title("TOOL_ID") +
  theme_minimal() 

3.4.2 Tool ID by Category

df <- df_tools 

# TOOL CHOICE BY STIMULUS
ggplot(data = df, aes( fill = fct_rev(TOOL_ID), x = STIMULUS_CATEGORY )) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  coord_flip() + 
  scale_fill_paletteer_d("awtools::a_palette", direction = 1)+
  scale_y_continuous(labels = scales::percent) + 
  labs( title = "TOOL ID by Category",
        subtitle = "", x = "") +
  easy_add_legend_title("TOOL_ID") +
  theme_minimal() 

3.4.3 Tool ID & Confidence by Stimulus

#FILTER DATAFRAME
df <- df_tools 

######### TOOL ID AND CONFIDENCE ##############
a <- ggplot (df, aes( x = fct_rev(STIMULUS), fill = fct_rev(TOOL_ID))) + 
  geom_bar(position = "fill", width = 0.8) +
  scale_fill_paletteer_d("awtools::a_palette", direction = 1) +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") +
  easy_add_legend_title("TOOL ID")+
  theme_minimal() 

# TOOL_CONFIDENCE by STIMULUS
b <- ggplot(df, aes( x = fct_rev(STIMULUS), y = TOOL_CONF, color = fct_rev(TOOL_ID))) + 
  geom_boxplot(position=position_dodge(0.9), width = 0.6)+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_paletteer_d("awtools::a_palette", direction = 1) +
  labs (x = "STIMULUS") + 
  easy_add_legend_title("TOOL ID")+
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'TOOL ID & CONFIDENCE by Stimulus',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.4.4 Tool ID & Confidence by Category

#FILTER DATAFRAME
df <- df_tools 

######### TOOL ID AND CONFIDENCE ##############
a <- ggplot (df, aes( x = fct_rev(STIMULUS_CATEGORY), fill = fct_rev(TOOL_ID))) + 
  geom_bar(position = "fill") +
  scale_fill_paletteer_d("awtools::a_palette", direction = 1) +
  # facet_grid( df$ENCOUNTER) + 
  labs (x = "") + 
  easy_add_legend_title("TOOL ID")+
  theme_minimal() 

# TOOL CONF 
b <- ggplot(df, aes( x = fct_rev(STIMULUS_CATEGORY), y = TOOL_CONF, color = fct_rev(TOOL_ID))) + 
  geom_boxplot(position=position_dodge(0.9))+
  geom_jitter(position=position_jitterdodge(), alpha = 0.2) + 
  scale_color_paletteer_d("awtools::a_palette", direction = 1) +
  labs (x = "STIMULUS CATEGORY") + 
  easy_add_legend_title("TOOL ID")+
  theme_minimal() 

(p <- (a / b )  + plot_annotation(
  title = 'TOOL ID & CONFIDENCE by Category',
  subtitle = '',
  caption = ''))

###################################################

rm(a,b,p)

3.5 ENCOUNTER CHOICE

3.5.1 Encounter Choice by Stimulus

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = fct_rev(STIMULUS), fill = ENCOUNTER)) +
  geom_bar(position = "fill") +
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  labs( title = "MAKER ENCOUNTER by Stimulus (grouped by CATEGORY) ",
        subtitle = "", x = "") +
  scale_fill_brewer(palette = "Dark2") + 
  # scale_fill_viridis(discrete=TRUE, option="viridis", direction = -1) +
  coord_flip()+
  # easy_add_legend_title("") +
  theme_minimal()

3.5.2 Encounter Choice by Category

df <- df_graphs 

#MAKER IDENTIFICATION
ggplot( df, aes( x = STIMULUS_CATEGORY, fill = ENCOUNTER)) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  labs( title = "ENCOUNTER by Stimulus Category (grouped by CATEGORY)",
        subtitle = "", x = "") +
  scale_fill_brewer(palette = "Dark2") + 
  coord_flip()+
  theme_minimal()

3.6 ACTIONS CHOICE

3.6.1 Actions Choice by Stimulus

df <- df_actions 

# ACTION CHOICE BY STIMULUS
ggplot(data = df, aes( fill = CHART_ACTION, x = fct_rev(STIMULUS) )) +
  geom_bar(position = "fill") +
  coord_flip() + 
  facet_grid(fct_rev(STIMULUS_CATEGORY) ~ Distribution, scales = "free", space = "free") + 
  scale_fill_paletteer_d("awtools::a_palette", direction = 1)+
  scale_y_continuous(labels = scales::percent) + 

  labs( title = "Chart Action by Stimulus (grouped by Category)",
        subtitle = "", x = "") +
  easy_add_legend_title("ACTION") +
  theme_minimal() 

3.6.2 Actions Choice by Category

df <- df_actions 

# ACTION CHOICE BY STIMULUS
ggplot(data = df, aes( fill = CHART_ACTION, x = STIMULUS_CATEGORY )) +
  geom_bar(position = "fill") +
  facet_grid(Distribution ~ .) + 
  coord_flip() + 
  scale_fill_paletteer_d("awtools::a_palette", direction = 1)+
  scale_y_continuous(labels = scales::percent) + 
  labs( title = "CHART ACTION by Category",
        subtitle = "", x = "") +
  easy_add_legend_title("ACTION") +
  theme_minimal() 

4 INTERACTIVE GRAPHS

4.0.1 WIP IXN Maker ID & Politics

#SETUP LISTS 
stim <- ref_stimuli
plots_maker_politics <- htmltools::tagList()

### MAKE PLOTS 
# LOOP THROUGH STIMULI
i = 0
for (s in stim){
  i = i+1 #iterator hack
  
  d <- df_graphs_full %>% filter(STIMULUS == s) %>% select(PID, STIMULUS, 
                                                               MAKER_ID, MAKER_DETAIL, MAKER_CONF, MAKER_POLITIC,
                                                               D_politicalParty, D_politicsSocial, D_politicsFiscal) 
  p <- ggplot(d, aes(x = MAKER_POLITIC, y = MAKER_CONF,
          color = MAKER_ID ,
          text = paste0("MAKER-DETAIL: ",MAKER_DETAIL, "<br>","ID: ", PID)))+
      geom_point() +
      xlim(0,100)+
      ylim(0,100)+
      facet_grid(MAKER_ID  ~ D_politicalParty) +
      theme_minimal() +
      labs(
        title = paste(s," | ", "MAKER Identification, Politics and Confidence, by Participant Confidence and Political Affiliation"),
        x = "MAKER POLITICS",
        y = "MAKER CONFIDENCE"
      )

  
  plots_maker_politics[[i]] <- ggplotly(p)
  
}

names(plots_maker_politics) <- ref_stimuli


#works in console but not render 
### PRINT PLOTS
# for (s in stim){
#   print(plots_maker_politics[[s]])
# }


# ggplotly(p) #works for single

plots_maker_politics

5 WIP QDA ANALYSIS

5.1 MAKER ID DETAIL

# df <- df_graphs_coded %>% select( ID.Prolific, STIMULUS, BLOCK, STIMULUS_CATEGORY, MAKER_ID, MAKER_DETAIL, CODE_M_ID_SPECIFIC) %>% 
#   separate_longer_delim(
#     cols = CODE_M_ID_SPECIFIC,
#     delim = ","
#   ) %>% filter(
#     CODE_M_ID_SPECIFIC %nin% c("x", "X","example")
#   ) %>% mutate(
#     CODED_MAKER = str_trim(CODE_M_ID_SPECIFIC, side="left"),
#     CODED_MAKER = str_to_upper(CODED_MAKER),
#     CODED_MAKER = factor(CODED_MAKER, levels = c( "NYT",
#                                                   "WASHINGTON POST" ,
#                                                   "USA TODAY" ,
#                                                   "THE ECONOMIST" ,
#                                                   "WSJ",
#                                                   "POPULAR SCIENCE" ,
#                                                   "TIME",
#                                                   "NPR",
#                                                   "PBS",
#                                                   "ASSOCIATED PRESS"  ,
#                                                   "BBC",
#                                                   "CBS NEWS"  ,
#                                                   "ABC NEWS"  ,
#                                                   "NBC",
#                                                   "CNN",
#                                                   "FOX NEWS"  ,
#                                                   "BUZZFEED",
#                                                   "VOX",
#                                                   "VICE",
#                                                   "HUFFINGTON POST" ,
#                                                   "US DOD"  ,
#                                                   "US BUREAU OF ECONOMIC ANALYSIS",
#                                                   "US DOE"  ,
#                                                   "US CDC"  ,
#                                                   "MINISTRY OF HEALTH"  ,
#                                                   "EPA",
#                                                   "NOAA",
#                                                   "NATIONAL WEATHER SERVICE"  ,
#                                                   "WHO",
#                                                   "UN",
#                                                   "THE NATURE CONSERVANCY"  ,
#                                                   "GREENPEACE",
#                                                   "LEAF",
#                                                   "HARVARD",
#                                                   "COLUMBIA UNIVERSITY" ,
#                                                   "IAA",
#                                                   "IBM",
#                                                   "MICROSOFT",
#                                                   "GOLDMAN SACHS" ,
#                                                   "GMC",
#                                                   "TESLA",
#                                                   "GREEN PARTY OF AMERICA"  ,
#                                                   "THE DEMOCRATIC PARTY"  ,
#                                                   "THE REPUBLICAN PARTY")),
#     STIM = factor(STIMULUS, levels = c(
#       "B1-1",  "B2-1", "B3-1", "B4-1", "B5-1", "B6-1",
#       "B1-2",  "B2-2", "B3-2", "B4-2", "B5-2", "B6-2",
#       "B1-3", "B2-3", "B3-3", "B4-3", "B5-3", "B6-3",
#       "B1-4","B2-4" ,"B3-4" ,"B4-4" ,"B5-4" ,"B6-4", "B0-0"
#     ))
#   ) 
# 
# 
# 
# ggplot(df,  aes(x = MAKER_ID, fill = CODED_MAKER )) + 
#   geom_bar(position = "stack") +
#   facet_grid( rows = vars(BLOCK), cols = vars(fct_rev(STIMULUS_CATEGORY))) + 
#   theme_minimal() + 
#   labs(title = "Specific makers identified by stimulus")
# 
#                           
# ggplot(df,  aes(x = STIMULUS, fill = CODED_MAKER )) + 
#   geom_bar(position = "stack") + 
#   theme_minimal()
# 
# 
# ggplot(df,  aes(x = STIMULUS, fill = CODED_MAKER )) + 
#   geom_bar(position = "stack") + 
#   facet_wrap(~CODED_MAKER) + 
#   theme_minimal() +
#   easy_remove_x_axis() +
#   easy_remove_legend()
# 
#   
# ggplot(df,  aes(x = CODE_M_ID_SPECIFIC, fill = STIM, )) + 
#   geom_bar(position = "stack") + 
#   coord_flip() + 
#   theme_minimal() 
#   

6 ALL SEMANTIC DIFFERENTIAL QUESTIONS

6.1 ALL QUESTIONS

6.1.1 ALL Q BY STIMULUS

##ONLY RENDER GRAPHS IF SET TO TRUE
if(graph_render == TRUE){


#################### ALL QUESTIONS AT STIMULUS #####################
# ONE PLOT FOR EACH STIMULUS WITH ALL QUESTIONS

#set stimuli to be graphed 
stimuli <- ref_stimuli #created in wrangling block
# stimili <- c("B0-0","B2-1" ,"B2-2", "B2-3", "B2-4")

plots_stimulus <- list()
i = 0
for (s in stimuli){
  i = i+1
  
  # setup dataframe 
  title <- df_stimuli %>% filter(ID == s) %>% select(NAME) 
  title <- paste(s,"|",title)
  df <- df_graphs %>% filter(STIMULUS== s)

  #subset data cols 
  cols <- df %>% select( all_of(ref_sd_questions))
  plots <- as.list(lapply(colnames(cols), plot_sd, data = df, type ="S", split = FALSE, boxplot=TRUE))

  #aggregate q plots into one for stimulus 
  x <- plots[[1]] / plots[[2]] / plots[[3]] / plots[[4]] / plots[[5]] / plots[[6]] / plots[[7]] /
   plots[[8]] /plots[[9]] /plots[[10]] /plots[[11]] + 
   plot_annotation(
     title = title,
     subtitle =""
   )
  
  plots_stimulus[[i]] <- x
  #ggsave(plot = x, path="figs/all_q_by_stimulus", filename =paste0(s,".png"), units = c("in"), width = 10, height = 14  )
}

#GIVE NAMES TO LIST
#NOW CAN ACCESS plots by plots_stimulus$`B1-1`
#ALSO plots_stimulus$`B1-1`[[1]]
names(plots_stimulus) <- stimuli
rm(x, i, plots)

#############################################################################
} 

6.1.1.1 WIP ALT SD GRAPHS

# # library
# library(ggdist)
# library(ggridges)
# 
# #RIDGEPLOT
# ##ALL QUESTIONS FOR STIMULUS 1
# 
# s = "B0-0"
# df <- df_sd_questions %>% select(1:6, s) %>% filter(!is.na(s)) %>%  mutate(
#   value = `B0-0`
# )
# 
# ggplot(df, aes(x = value, y = QUESTION, fill = QUESTION)) +
#   geom_density_ridges(scale=0.9) +
#   # xlim(0,100)+
#   theme_ridges() + 
#   theme(legend.position = "none")
# 
# 
# 
# 
# library(ggdist)
# 
# d <- df %>% filter(str_detect(QUESTION,"CHART_"))
# 
# ggplot(df, aes(y = QUESTION, x = value, fill = QUESTION)) +
#   stat_slab(aes(thickness = after_stat(pdf*n)), scale = 0.7) +
#   stat_dotsinterval(side = "bottom", scale = 0.7, slab_linewidth = NA) +
#   theme_minimal()
# 

6.1.2 ALL Q BY CATEGORY

##ONLY RENDER GRAPHS IF SET TO TRUE
if(graph_render == TRUE){

#################### ALL QUESTIONS AT CATEGORY #####################
# ONE PLOT FOR EACH CATEGORY WITH ALL QUESTIONS

#set questions to be graphed 
categories <- unique(df_graphs$STIMULUS_CATEGORY)#created in wrangling block
plots_category <- list()
i = 0
for (c in categories){
  i = i+1

  # setup dataframe
  title <- paste("STIMULUS CATEGORY ", c)
  df <- df_graphs %>% filter(STIMULUS_CATEGORY == c)
  
  #subset data cols
  cols <- df %>% select( all_of(ref_sd_questions))
  plots <- as.list(lapply(colnames(cols), plot_sd, data = df, type = "S", split = FALSE, boxplot=TRUE))
  
  #aggregate stimulus plots into block for question
  x <- plots[[1]] / plots[[2]]  / plots[[3]] / plots[[4]] / plots[[5]] / plots[[6]] / plots[[7]] /
        plots[[8]] /plots[[9]] /plots[[10]] /plots[[11]] +
      plot_annotation(
        title = title,
       subtitle =""
      )

  plots_category[[i]] <- x
  ggsave(plot = x, path="figs/all_q_by_category", filename =paste0(c,".png"), units = c("in"), width = 10, height = 26)
  
}  

#GIVE NAMES TO LIST
#NOW CAN ACCESS plots by plots_category$A
#ALSO plots_category$A[[1]]
names(plots_category) <- categories
rm(x, i, plots)

}

6.2 BY QUESTION

6.2.1 Q for ALL STIMULI

##ONLY RENDER GRAPHS IF SET TO TRUE
if(graph_render == TRUE){

####################CREATE SD PLOTS FOR EACH QUESTION#####################

#set questions to be graphed 
questions <- ref_sd_questions  #created in wrangling block
plots_s_question <- list()
i = 0
for (q in questions){
  i = i+1
  
  # setup dataframe 
  title <- paste(q)
  df <- df_sd_questions %>% filter(QUESTION == q)

  #subset data cols 
  c <- df %>% select( all_of(ref_stimuli))
  plots <- as.list(lapply(colnames(c), plot_sd, data = df, type = "Q", split = FALSE, boxplot=TRUE))
  
  #aggregate stimulus plots into block for question
  x <- plots[[1]] / plots[[2]]  / plots[[3]] / plots[[4]] / plots[[5]] / plots[[6]] / plots[[7]] /
        plots[[8]] /plots[[9]] /plots[[10]] /plots[[11]] /  plots[[12]] / plots[[13]] / plots[[14]] / 
        plots[[15]] /plots[[16]] /plots[[17]] /plots[[18]] /  plots[[19]] / plots[[20]] / plots[[21]] /
        plots[[22]] /plots[[23]] /plots[[24]] /plots[[25]] + 
      plot_annotation(
        title = title,
       subtitle =""
      )
  plots_s_question[[i]] <- x 
  ggsave(plot = x, path="figs/by_q_for_all_stimuli", filename =paste0(q,".png"), units = c("in"), width = 10, height = 26)
}

#GIVE NAMES TO LIST
#NOW CAN ACCESS plots by plots_s_question$MAKER_DESIGN
#ALSO plots_s_question$MAKER_DESIGN[[1]]
names(plots_s_question) <- questions
rm(x, i, plots)

#############################################################################

}

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 254 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 254 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 255 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 256 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_point()`).

6.2.2 Q for ALL CATEGORIES

##ONLY RENDER GRAPHS IF SET TO TRUE
if(graph_render == TRUE){
  
####################SD PLOTS QUESTION BY CATEGORY #####################

#set grouping column 
g = "STIMULUS_CATEGORY" #string name of column to group by 

#set questions to be graphed 
questions <- ref_sd_questions#created in wrangling block
plots_c_question <- list()

i = 0
for (q in questions){
  i = i+1
  left <-   ref_labels[q,]$left
  right <-  ref_labels[q,]$right
  
  # setup dataframe 
  title <- paste(q," BY CATEGORY")
  df <- df_graphs 
  
  x <- multi_sd(df, left, right, x = q, y = g, color = g) + 
      labs(title  = q) 
 
  plots_c_question[[i]] <- x
  ggsave(plot = x, path="figs/by_q_for_all_categories", filename =paste0(q,".png"))
}

#GIVE NAMES TO LIST
#NOW CAN ACCESS plots by plots_c_question$MAKER_DESIGN
#ALSO plots_c_question$MAKER_DESIGN[[1]]
names(plots_c_question) <- questions
rm(x, i)
  
#############################################################################

}

## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image

7 LEGACY

# #### 
# q = "MAKER_DESIGN"
# df <- df_questions %>% filter(QUESTION == q) %>% 
#   mutate(
#     value = as.numeric(value),
#     STIMULUS_CATEGORY = str_remove(STIMULUS,"B.-"),
#     STIMULUS_CATEGORY = factor(STIMULUS_CATEGORY,
#                   levels=c("0","1","2","3","4"),
#                   labels= c("F","A","B","C","D"))
# )
# 
# 
# g <- ggplot(df, aes(y = value, x="", color = STIMULUS_CATEGORY)) +
#     geom_boxplot(width = 0.5)+
#     geom_jitter(width = 0.1, alpha=0.3) + 
#     scale_y_continuous(limits=c(-1,101)) +
#     labs(x="", y="") +
#     coord_flip()  +
#     facet_grid(df$STIMULUS_CATEGORY ~ .) + 
#     labs(title=df$STIMULUS_CATEGORY) + 
#     guides(
#         y = guide_axis_manual(labels = ref_labels[q,"left"]),
#         y.sec = guide_axis_manual(labels = ref_labels[q,"right"])
#       ) +  
#   theme_minimal()  + 
#   labs (title = q)
# 
# g
# left <- rep(ref_labels[q,"left"], length(unique(df$STIMULUS_CATEGORY)))
# right <- rep(ref_labels[q,"left"], length(unique(df$STIMULUS_CATEGORY)))
# a <- multi_sd(df, x = value, y = QUESTION, color = STIMULUS_CATEGORY, left=, right=right)
# 
# 
# multi_sd <- function (df, x, y, color, left,right) { 
#   
#   g <- ggplot(df, aes(y = {{x}}, x = {{y}}, color = {{color}}))+
#   geom_boxplot(width = 0.5) + 
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   facet_grid(rows=vars({{color}})) +
#   labs(x="", y="") +
#   coord_flip() + 
#   guides(
#     y = guide_axis_manual(labels = left),
#     y.sec = guide_axis_manual(labels = right)
#   ) + theme_minimal() 
#   
#   return(g)
# }




# ## GROUPED PLOTSD FUNCTION 
# grouped_plotsd = function (data, x, type, q, boxplot) {
#   ggplot(df, aes(y = .data[[x]], x="")) +
#     {if(boxplot) geom_boxplot(width = 0.5) } +
#     geom_jitter(width = 0.1, alpha=0.3) + 
#     scale_y_continuous(limits=c(-1,101)) +
#     labs(x="", y="") +
#     coord_flip()  +
#     {if(type == "Q")
#       guides(
#         y = guide_axis_manual(labels = ref_labels[q,"left"]),
#         y.sec = guide_axis_manual(labels = ref_labels[q,"right"])
#       )} +  
#   theme_minimal()  + 
#      labs (
#        caption = column
#      )
# }


#####################################################################

# # stim <- "B1-1"
# 
# # setup dataframe 
# title <- df_stimuli %>% filter(ID ==stim) %>% select(NAME) 
# title <- paste(stim,"|",title)
# df <- df_graphs %>% filter(STIMULUS==stim)
# 
# #subset data cols 
# d <- df %>% select( all_of(questions))
# plots <- as.list(lapply(colnames(d), plotsd, data = df, boxplot=TRUE))
# 
# ## PICK UP HERE, FIGURE OUT HOW TO LOOP OVER STIM
# 
# x <- plots[[1]] / plots[[2]] / plots[[3]] / plots[[4]] / plots[[5]] / plots[[6]] / plots[[7]] /
#   plots[[8]] /plots[[9]] /plots[[10]] /plots[[11]] + 
#   plot_annotation(
#     title = title,
#     subtitle =""
#   )
# x
# ggsave(plot = x, filename =paste(stim,".png"), units = c("in"), width = 10, height = 14  )




#CREATE MATRIX 
# m <- matrix(nrow = length(stim), ncol = length(questions)) 
# rownames(m) <- stim
# colnames(m) <- questions

7.1 LEGACY

8 MODELLING

##STATSPLOT
# grouped_ggwithinstats(
#   data  = df_graphs,
#   x     = STIMULUS,
#   y     = CHART_TRUST,
#   grouping.var = ID.Study
# ) 

# grouped_ggscatterstats(
#   data = df_graphs, ## data frame from which variables are taken
#   x = CHART_BEAUTY, ## predictor/independent variable
#   y = CHART_TRUST, ## dependent variable
#   grouping.var = ID.Study,
#   xlab = "CHART BEAUTY", ## label for the x-axis
#   ylab = "CHART TRUST", ## label for the y-axis
#   # label.expression = rating < 5 & budget > 100, ## expression for deciding which points to label
#   point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
#   xfill = "#CC79A7", ## fill for marginals on the x-axis
#   yfill = "#009E73" ## fill for marginals on the y-axis
#   # title = "CHART TRUST (VS) MAKER TRUST",
#   # caption = ""
# )
# 
# gf_point( data = df_graphs, CHART_TRUST~MAKER_TRUST, color = ~STIMULUS) %>% gf_facet_wrap(~STIMULUS)
# 
# 
# grouped_ggscatterstats(
#   data = df_graphs, ## data frame from which variables are taken
#   x = MAKER_TRUST, ## predictor/independent variable
#   y = CHART_TRUST, ## dependent variable
#   grouping.var = ID.Study,
#   xlab = "MAKER TRUST", ## label for the x-axis
#   ylab = "CHART TRUST", ## label for the y-axis
#   # label.expression = rating < 5 & budget > 100, ## expression for deciding which points to label
#   point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
#   xfill = "#CC79A7", ## fill for marginals on the x-axis
#   yfill = "#009E73" ## fill for marginals on the y-axis
#   # title = "CHART TRUST (VS) MAKER TRUST",
#   # caption = ""
# )
# 
# gf_point( data = df_graphs, MAKER_TRUST~CHART_TRUST, color = ~STIMULUS) %>% gf_facet_wrap(~STIMULUS)
# 
# 
# 
# grouped_ggscatterstats(
#   data = df_graphs, ## data frame from which variables are taken
#   x = CHART_BEAUTY, ## predictor/independent variable
#   y = MAKER_ALIGN, ## dependent variable
#   grouping.var = ID.Study,
#   xlab = "CHART BEAUTY", ## label for the x-axis
#   ylab = "MAKER ALIGN", ## label for the y-axis
#   # label.expression = rating < 5 & budget > 100, ## expression for deciding which points to label
#   point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
#   xfill = "#CC79A7", ## fill for marginals on the x-axis
#   yfill = "#009E73" ## fill for marginals on the y-axis
#   # title = "CHART TRUST (VS) MAKER TRUST",
#   # caption = ""
# )
# 
# 
# gf_point( data = df_graphs, MAKER_ALIGN~CHART_BEAUTY, color = ~STIMULUS) %>% gf_facet_wrap(~STIMULUS)
# 
# 
# 
# 
# 
# 
# ggscatterstats(
#   data = df_graphs %>% filter(STIMULUS=="B0-0"), ## data frame from which variables are taken
#   x = CHART_BEAUTY, ## predictor/independent variable
#   y = MAKER_TRUST, ## dependent variable
#   xlab = "CHART BEAUTY", ## label for the x-axis
#   ylab = "MAKER TRUST", ## label for the y-axis
#   # label.expression = rating < 5 & budget > 100, ## expression for deciding which points to label
#   point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
#   xfill = "#CC79A7", ## fill for marginals on the x-axis
#   yfill = "#009E73" ## fill for marginals on the y-axis
#   # title = "CHART TRUST (VS) MAKER TRUST",
#   # caption = ""
# )
# 
# gf_point( data = df_graphs %>% filter(STIMULUS=="B0-0"), CHART_TRUST~CHART_BEAUTY, color = ~STIMULUS) %>% gf_facet_wrap(~STIMULUS)
# 
# gf_point( data = df_graphs %>% filter(STIMULUS=="B0-0"), MAKER_TRUST~CHART_BEAUTY, color = ~STIMULUS) %>% gf_facet_wrap(~STIMULUS)

# 
# 
# #TODO CENTERING AND NORMALIZING
# 
# ### little model
# df <- df_graphs %>% filter(STIMULUS == "B0-0")
# m <- lm( CHART_TRUST ~ CHART_BEAUTY,data = df)
# summary(m)
# ggnostic(m) #GGALLY MODEL CHECKS
# check_model(m) #EASY STATS MODEL CHECKS 
# 
# report(m)

### MORE COMPLEX MODEL
# df <- df_graphs %>% filter(STIMULUS != "B0-0")
# m1 <- lmer(CHART_TRUST ~ CHART_BEAUTY + (1|ID.Qualtrics), data = df)
# m2 <- lmer(CHART_TRUST ~ CHART_BEAUTY + (1|STIMULUS), data = df)
# m3 <- lmer(CHART_TRUST ~ CHART_BEAUTY + (1 | STIMULUS) + (1 | ID.Qualtrics), data = df)
# summary(m1)
# summary(m2)
# summary(m3)
# compare_parameters(m1,m2,m3)
# compare_performance(m1,m2,m3, rank = TRUE)
# 
# 
# m <- lmer(CHART_TRUST ~ CHART_BEAUTY + MAKER_TRUST + (1 | STIMULUS) ,  data = df)
# mi <- lmer(CHART_TRUST ~ CHART_BEAUTY * MAKER_TRUST + (1 | STIMULUS) , data = df)
# summary(m)
# summary(mi)
# 
# compare_performance(m3,m,mi, rank = TRUE)
# 
# report(m)
#todo see https://yury-zablotski.netlify.app/post/mixed-effects-models-2/ about fitting with ML vs REML for model comparison 

# #TODO WALK THROUGH THIS 
# #https://yury-zablotski.netlify.app/post/mixed-models/#multiple-random-slope-model
# 
# #load example data
# data("sleepstudy")
# 
# #fit the model
# m_slp <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# 
# #the next line put all the estimated intercept and slope per subject into a dataframe
# reaction_slp <- as.data.frame(t(apply(ranef(m_slp)$Subject, 1,function(x) fixef(m_slp) + x)))
# 
# #to get the predicted regression lines we need one further step, writing the linear equation: Intercept + Slope*Days with different coefficient for each subject
# pred_slp <- melt(apply(reaction_slp,1,function(x) x[1] + x[2]*0:9), value.name = "Reaction")
# 
# #some re-formatting for the plot
# names(pred_slp)[1:2] <- c("Days","Subject")
# pred_slp$Days <- pred_slp$Days - 1
# pred_slp$Subject <- as.factor(pred_slp$Subject)
# 
# #plot with actual data 
# ggplot(pred_slp,aes(x=Days,y=Reaction,color=Subject))+
#   geom_line()+
#   geom_point(data=sleepstudy,aes(x=Days,y=Reaction))+
#   facet_wrap(~Subject,nrow=3)

9 STASH

9.0.1 COLOR palettes

#DISPLAY COLOR PALETTE
#display.brewer.pal(n = 8, name = 'Dark2')

#DISPLAY COLOR PALETTE
# paletteer_d("Redmonder::dPBIPuGn")

9.0.2 DISTRIBUTION plots

# #SET UP DATAFRAME
# df <- df_data
# 
# ## DENSITY HISTOGRAPH
# ggplot(data = df_data, aes( x = duration.min, fill = Assigned.Block)) +
#   geom_density(alpha = 0.5) +
#   facet_grid(rows = vars(Assigned.Block)) +
#   labs( x = "Survey Response Time (mins)",
#         title = "TOTAL Response Time by Sample",
#         subtitle = "(expect similiar across samples)") +
#   easy_add_legend_title("Sample") +
#   theme_minimal()
# 
# 
# ## RAINCLOUD
# ggplot(df, aes(x = duration.min, y = fct_rev(Assigned.Block), fill = fct_rev(Assigned.Block))) +
#   stat_slab(aes(thickness = after_stat(pdf*n), alpha=0.5), scale = 0.7) +
#   stat_dotsinterval(side = "bottom", scale = 0.7, slab_linewidth = NA) +
#   scale_fill_discrete(direction=-1)+
#   xlim(0,225) +
#   labs( x = "Survey Response Time (mins)", y="",
#         title = "TOTAL Response Time by Sample",
#         subtitle = "(expect similiar across samples)") +
#   theme_minimal() +
#   easy_remove_legend()
# 
# 
# ##RIDGEPLOT
# ggplot(df, aes(x = duration.min, y = fct_rev(Assigned.Block), fill = fct_rev(Assigned.Block))) +
#   geom_density_ridges(scale=0.9) +
#   # geom_boxplot()+
#   stat_pointinterval()+
#   theme_ridges() +
#   scale_fill_discrete(direction=-1)+
#   theme(legend.position = "none") +
#     labs( x = "Survey Response Time (mins)", y="",
#         title = "TOTAL Response Time by Sample",
#         subtitle = "(expect similiar across samples)")

9.0.3 all-q for stimulus

# s = "B0-0" #stimulus code
# t = "STIMULUS B0 — MILLENIAL PINK PLANTS" #STIMULUS TITLE
# 
# #FILTER DATAFRAME
# df <- df_graphs %>% filter(STIMULUS == s)
# 
# 
# #BUILD SEMANTIC DIFFERENTIALS
# m_design <- single_sd(df, "professional", "layperson", x = MAKER_DESIGN) +
#       labs(title  = "MAKER-DESIGN") +
#       easy_remove_legend()
# 
# m_data <- single_sd(df, "professional", "layperson", x = MAKER_DATA) +
#       labs(title  = "MAKER-DATA") +
#       easy_remove_legend()
# 
# m_politics <- single_sd(df, "left-leaning", "right-leaning", x = MAKER_POLITIC) +
#       labs(title  = "MAKER-POLITICS") +
#       easy_remove_legend()
# 
# m_argue <- single_sd(df, "confrontational", "diplomatic", x = MAKER_ARGUE) +
#       labs(title  = "MAKER-ARGUE") +
#       easy_remove_legend()
# 
# m_selfish <- single_sd(df, "altruistic", "selfish", x = MAKER_SELF) +
#       labs(title  = "MAKER-SELFISH") +
#       easy_remove_legend()
# 
# m_align <- single_sd(df, "does NOT share", "does share", x = MAKER_ALIGN) +
#       labs(title  = "MAKER-ALIGNMENT") +
#       easy_remove_legend()
# 
# m_trust <- single_sd(df, "untrustworthy", "trustworthy", x = MAKER_TRUST) +
#       labs(title  = "MAKER-TRUST") +
#       easy_remove_legend()
# 
# 
# #BUILD MASTER PLOT
# PLOT_maker <- (m_design / m_data / m_politics / m_argue / m_selfish / m_align / m_trust) +
#   plot_annotation(
#     title = t,
#     subtitle =""
#   )
# 
# PLOT_maker

9.0.4 all-q for stimulus - grouped

#  s = "B0-0" #stimulus code
#  t = "STIMULUS B0 — MILLENIAL PINK PLANTS" #STIMULUS TITLE
#  g = "Assigned.Block"
#  ##can also use y and color to split by additional variable 
#  
#  #FILTER DATAFRAME
#  df <- df_graphs %>% filter(STIMULUS == s)
# 
#  #BUILD SEMANTIC DIFFERENTIALS
#  m_design <- multi_sd(df, "professional", "layperson", x = "MAKER_DESIGN", y = g, color = g) +
#        labs(title  = "MAKER-DESIGN") +
#        easy_remove_legend()
# 
#  m_data <- multi_sd(df, "professional", "layperson", x = "MAKER_DATA", y = g, color = g) +
#        labs(title  = "MAKER-DATA") +
#        easy_remove_legend()
# 
#  m_politics <- multi_sd(df, "left-leaning", "right-leaning", x = "MAKER_POLITIC", y = g, color = g) +
#        labs(title  = "MAKER-POLITICS") +
#        easy_remove_legend()
# 
#  m_argue <- multi_sd(df, "confrontational", "diplomatic", x = "MAKER_ARGUE", y = g, color = g) +
#        labs(title  = "MAKER-ARGUE") +
#        easy_remove_legend()
# 
#  m_selfish <- multi_sd(df, "altruistic", "selfish", x = "MAKER_SELF", y = g, color = g) +
#        labs(title  = "MAKER-SELFISH") +
#        easy_remove_legend()
# 
#  m_align <- multi_sd(df, "does NOT share", "does share", x = "MAKER_ALIGN", y = g, color = g) +
#        labs(title  = "MAKER-ALIGNMENT") +
#        easy_remove_legend()
# 
#  m_trust <- multi_sd(df, "untrustworthy", "trustworthy", x = "MAKER_TRUST", y = g, color = g) +
#        labs(title  = "MAKER-TRUST") 
# 
# 
#  #BUILD MASTER PLOT
#  PLOT_maker <- (m_design / m_data / m_politics / m_argue / m_selfish / m_align / m_trust) +
#    plot_annotation(
#      title = t,
#      subtitle =""
#    )
# 
#  PLOT_maker

9.0.5 across Blocks

######### MAKER ATTRIBUTES ########################
# vals = c("datacollar", "bluecollar")
# leftside <- rep ("PROFESSIONAL",n_blocks )
# rightside <-rep ("LAYPERSON",n_blocks ) 
# b00_m_design <- ggplot(df, aes(y = MAKER_DESIGN, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DESIGN")

# b00_m_data <- ggplot(df, aes(y = MAKER_DATA, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DATA")


# leftside <- rep ("LEFT-WING",n_blocks )
# rightside <-rep ("RIGHT-WING",n_blocks ) 
# b00_m_politics <- ggplot(df, aes(y = MAKER_POLITIC, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-POLITICS")
  

# leftside <- rep ("CONFRONTATIONAL",n_blocks )
# rightside <-rep ("DIPLOMATIC",n_blocks ) 
# b00_m_argue <- ggplot(df, aes(y = MAKER_ARGUE, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-ARGUE")
  
# leftside <- rep ("ALTRUSITC",n_blocks )
# rightside <-rep ("SELFISH",n_blocks ) 
# b00_m_selfish <- ggplot(df, aes(y = MAKER_SELF, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SELFISH")

# 
# leftside <- rep ("Does NOT",n_blocks )
# rightside <-rep ("DOES",n_blocks ) 
# b00_m_align <- ggplot(df, aes(y = MAKER_ALIGN, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SHARES-MY-VALUES")

# leftside <- rep ("UNTRUSTWORTHY",n_blocks )
# rightside <-rep ("TRUSTWORTHY",n_blocks ) 
# b00_m_trust <- ggplot(df, aes(y = MAKER_TRUST, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   # easy_remove_legend() +
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-TRUST")
# 
# PLOT_b0_maker <- (b00_m_design / b00_m_data / b00_m_politics / b00_m_argue / b00_m_selfish / b00_m_align / b00_m_trust) + 
#   plot_annotation(
#     title = "STIMULUS B0 — MILLENIAL PINK PLANTS",
#     subtitle =""
#   )
# 
# PLOT_b0_maker
###################################################

9.0.6 single Block

# #FILTER DATAFRAME
# df <- df_graphs %>% filter(STIMULUS == "B0-0")
# 
# ######### MAKER ATTRIBUTES ########################
# vals = c("datacollar", "bluecollar")
# leftside <- rep ("PROFESSIONAL",1 )
# rightside <-rep ("LAYPERSON",1 ) 
# b_m_design <- ggplot(df, aes(y = MAKER_DESIGN, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DESIGN")
# 
# b_m_data <- ggplot(df, aes(y = MAKER_DATA, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DATA")
# 
# 
# leftside <- rep ("LEFT-WING",1 )
# rightside <-rep ("RIGHT-WING",1 ) 
# b_m_politics <- ggplot(df, aes(y = MAKER_POLITIC, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-POLITICS")
#   
# 
# leftside <- rep ("CONFRONTATIONAL",1 )
# rightside <-rep ("DIPLOMATIC",1 ) 
# b_m_argue <- ggplot(df, aes(y = MAKER_ARGUE, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-ARGUE")
#   
# leftside <- rep ("ALTRUSITC",1 )
# rightside <-rep ("SELFISH",1 ) 
# b_m_selfish <- ggplot(df, aes(y = MAKER_SELF, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SELFISH")
# 
# 
# leftside <- rep ("Does NOT",1 )
# rightside <-rep ("DOES",1 ) 
# b_m_align <- ggplot(df, aes(y = MAKER_ALIGN, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SHARES-MY-VALUES")
# 
# leftside <- rep ("UNTRUSTWORTHY",1 )
# rightside <-rep ("TRUSTWORTHY",1 ) 
# b_m_trust <- ggplot(df, aes(y = MAKER_TRUST, x = ""))+
#   geom_boxplot(width = 0.5) +
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-TRUST")
# 
# 
# PLOT_b_maker <- (b_m_design / b_m_data / b_m_politics / b_m_argue / b_m_selfish / b_m_align / b_m_trust) + 
#   plot_annotation(
#     title = paste(unique(df$STIMULUS), "MAKER")
#   )
# 
# PLOT_b_maker
###################################################

9.0.7 single Block grouped

# 
# #FILTER DATAFRAME
# df <- df_graphs %>% filter(STIMULUS == "B0-0")
# 
# ######### MAKER ATTRIBUTES ########################
# vals = c("datacollar", "bluecollar")
# leftside <- rep ("PROFESSIONAL",1 )
# rightside <-rep ("LAYPERSON",1 ) 
# b_m_design <- ggplot(df, aes(y = MAKER_DESIGN, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DESIGN")
# 
# b_m_data <- ggplot(df, aes(y = MAKER_DATA, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-DATA")
# 
# 
# leftside <- rep ("LEFT-WING",1 )
# rightside <-rep ("RIGHT-WING",1 ) 
# b_m_politics <- ggplot(df, aes(y = MAKER_POLITIC, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-POLITICS")
#   
# 
# leftside <- rep ("CONFRONTATIONAL",1 )
# rightside <-rep ("DIPLOMATIC",1 ) 
# b_m_argue <- ggplot(df, aes(y = MAKER_ARGUE, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-ARGUE")
#   
# leftside <- rep ("ALTRUSITC",1 )
# rightside <-rep ("SELFISH",1 ) 
# b_m_selfish <- ggplot(df, aes(y = MAKER_SELF, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SELFISH")
# 
# 
# leftside <- rep ("Does NOT",1 )
# rightside <-rep ("DOES",1 ) 
# b_m_align <- ggplot(df, aes(y = MAKER_ALIGN, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-SHARES-MY-VALUES")
# 
# leftside <- rep ("UNTRUSTWORTHY",1 )
# rightside <-rep ("TRUSTWORTHY",1 ) 
# b_m_trust <- ggplot(df, aes(y = MAKER_TRUST, x = ID.Study, color = ID.Study))+
#   geom_jitter(width = 0.1, alpha=0.5) + 
#   scale_y_continuous(limits=c(-1,101)) +
#   labs(x="", y="") +
#   coord_flip() + 
#   theme_minimal() + 
#   easy_remove_legend() + 
#   guides(
#   y = guide_axis_manual(
#   #breaks = vals, 
#   labels = leftside  
#   ),
#   y.sec = guide_axis_manual(
#   #breaks = vals, 
#   labels = rightside
# )) + 
#   labs(title  = "MAKER-TRUST")
# 
# 
# PLOT_b_maker <- (b_m_design / b_m_data / b_m_politics / b_m_argue / b_m_selfish / b_m_align / b_m_trust) + 
#   plot_annotation(
#     title = paste(unique(df$STIMULUS), "MAKER")
#   )
# 
# PLOT_b_maker
# ###################################################